阅读说明：本技术 一种智能纺纱生产线故障检测系统 (Intelligent spinning production line fault detection system ) 是由 解艳芳 梅剑香 于 2020-04-22 设计创作，主要内容包括：本发明公开了一种智能纺纱生产线故障检测系统,通过数据采集模块和数据处理模块对运行速度和振动信号进行采集和处理,并提取运行速度参数和特征振动信号参数作为状态识别模块的输入向量；状态识别分模型根据相应的输入向量识别各工序的运行状态,然后通过状态识别总模型对纺纱生产线的运行状态进行识别,并通过状态预警模块发出相应的状态预警信号。本发明通过状态识别分模型和状态识别总模型的双层识别,能够实现纺纱生产线运行状态的全面和可靠识别,从而及时对潜在故障进行预警,减少生产线故障对纺纱效率和质量的影响。(The invention discloses an intelligent spinning production line fault detection system, which is characterized in that a data acquisition module and a data processing module are used for acquiring and processing running speed and vibration signals, and a running speed parameter and a characteristic vibration signal parameter are extracted as input vectors of a state recognition module; the state recognition sub-model recognizes the running state of each process according to the corresponding input vector, then recognizes the running state of the spinning production line through the state recognition total model, and sends out a corresponding state early warning signal through the state early warning module. According to the invention, through double-layer recognition of the state recognition sub-model and the state recognition total model, the comprehensive and reliable recognition of the spinning production line running state can be realized, so that the early warning is timely carried out on potential faults, and the influence of the production line faults on the spinning efficiency and quality is reduced.)

1. An intelligent spinning production line fault detection system is characterized by comprising a data acquisition module, a data processing module, a state identification module and a fault early warning module which are sequentially connected; wherein the content of the first and second substances,

the data acquisition module acquires running state signals of the spinning equipment through a sensor and sends the acquired state signals to the data processing module;

the data processing module carries out noise reduction processing on the running state signal and then extracts characteristic signal parameters which can reflect the running state of the spinning equipment most;

the state recognition module takes the characteristic signal parameters as input vectors, recognizes the running state of the spinning equipment by using a state recognition model, and sends corresponding state early warning indication signals to the fault early warning module;

and the fault early warning module sends out a corresponding fault early warning signal according to the state early warning indication signal.

2. An intelligent spinning production line fault detection system according to claim 1, wherein the data acquisition module comprises a blowing-carding data acquisition unit, a coarse data acquisition unit, a fine data acquisition unit and a spooling data acquisition unit; wherein the content of the first and second substances,

the blowing-carding data acquisition unit is used for acquiring running state signals of an opening blowing box, a cotton feeding box and a carding machine;

the coarse data collecting unit is used for collecting running state signals of the drawing frame and the roving frame;

the spun yarn data acquisition unit is used for acquiring running state signals of a spinning frame;

the winding data acquisition unit is used for acquiring running state signals of the winding machine.

3. An intelligent spinning production line fault detection system according to claim 1, wherein the state identification models comprise a state identification sub-model and a state identification total model, and the state identification sub-model comprises an open-carding state identification model, a coarse state identification model, a spun yarn state identification model and a spooling state identification model; wherein the content of the first and second substances,

the blowing-carding unit state identification model is used for identifying the operation states of the opening blowing box, the cotton feeding box and the carding machine according to the operation state signals collected by the blowing-carding unit data collection unit;

the rough state identification model is used for identifying the running states of the drawing frame and the roving frame according to the running state signals collected by the rough data collecting unit;

the spun yarn state identification model is used for identifying the running state of the spinning frame according to the running state signals collected by the spun yarn data collection unit;

the winding state identification model is used for identifying the operation state of the winding machine according to the operation state signals collected by the winding data collection unit;

and the state recognition total model is used for comprehensively recognizing the running state of the spinning production line according to the recognition result of the state recognition sub-model.

4. An intelligent spinning production line fault detection system according to any one of claims 1 to 3, wherein the sensors comprise a speed sensor and a vibration sensor, and the operation state signals comprise an operation speed signal and a vibration frequency signal.

5. An intelligent spinning production line fault detection system according to claim 4, wherein the data processing module performs noise reduction processing on the vibration frequency signal and then extracts a characteristic vibration signal parameter which can most reflect the running state of the spinning equipment; the data processing module extracts an operation speed parameter from the operation speed signal; and the data processing module sends the characteristic vibration signal parameter and the running speed parameter to the state identification module together.

6. An intelligent spinning production line fault detection system according to claim 5, wherein the characteristic vibration signal parameter extraction method comprises the following steps:

(1) performing discrete wavelet transform on the vibration signals to obtain the wavelet coefficients of the vibration signals of all scales;

(2) performing noise reduction processing on the wavelet coefficients of the vibration signals of all scales by using a wavelet noise reduction technology to obtain the wavelet coefficients and scale coefficients of the vibration signals;

(3) obtaining a vibration signal characteristic scale parameter distributed along the scale according to the wavelet coefficient and the scale coefficient, and then constructing a vibration signal characteristic scale vector;

(4) extracting a vibration signal characteristic scale parameter which can best reflect the fault characteristic of the spinning equipment from the vibration signal characteristic scale vector to construct a characteristic vibration signal parameter vector;

the characteristic scale parameter of the vibration signal which can best reflect the fault characteristic of the spinning equipment is determined by the frequency of the vibration signal when the spinning equipment is in normal operation.

7. An intelligent spinning production line fault detection system according to claim 5, wherein the state recognition module takes the characteristic vibration signal parameter and the running speed parameter as input vectors, respectively recognizes the running states of the corresponding spinning equipment by using the state recognition sub-models, comprehensively recognizes the running states of the spinning production line by using the state recognition total model, and then sends corresponding state early warning indication signals to the fault early warning module.

8. The system as claimed in claim 7, wherein the state recognition submodel includes a spinning device state distribution probability vector χ, a spinning device state transition probability matrix A, an observed value probability distribution U and a state dwell time probability distribution V, which are recorded as Φ_r＝(χ_r，A_r，U_r，V_r) R is a positive integer of 1 to 4, phi₁、Φ₂、Φ₃And phi₄Respectively representing a blowing-carding state identification model, a coarse state identification model, a spun yarn state identification model and a spooling state identification model.

9. An intelligent spinning production line fault detection system according to claim 8, wherein the state identification submodel firstly trains the model through the spinning equipment historical operation state signals collected by the data collection module to obtain model parameters of the spinning equipment in each state; and then, determining the current operation state model parameters of the spinning equipment according to the current operation state signals of the spinning equipment acquired by the data acquisition module, and judging the current operation state of the spinning equipment according to the matching degree of the current model parameters of the spinning equipment and the model parameters of the spinning equipment in each state.

10. An intelligent spinning production line fault detection system according to claim 7, wherein the state recognition total model obtains current spinning equipment of each process according to the state recognition sub-modelOperating state S_rThe operation state weight ω and the process relation R of each process determine the operation state of the spinning line, and are represented by ψ (S)_rω, R), R is a positive integer from 1 to 4.

Technical Field

The invention belongs to the technical field of fault detection, and particularly relates to an intelligent spinning production line fault detection system.

Background

The intelligent spinning production line generally comprises four production processes of carding, roving, spinning and spooling according to a spinning process flow, and intelligent control of the spinning production line is realized through a sensing technology, an information fusion technology and an industrial internet technology. Once a certain device of a certain process breaks down, the quality and the spinning efficiency of the yarn are seriously influenced, and even the device and the personal safety are threatened. However, when a part of a process of the production line fails but the operation of the whole production line is not affected, the failure is not easily found in time, but the product becomes a waste product due to the potential failure of the process, and great loss is caused. Therefore, an intelligent spinning production line fault detection system is urgently needed to provide guarantee for smooth spinning procedures.

The existing fault detection technology generally relies on a multi-sensing technology, a sensor is used for collecting equipment operation state signals, then a signal processing technology is used for extracting useful signals to serve as indication signals for judging the equipment operation state, and the equipment operation state is identified through signal fluctuation. However, due to the complexity, randomness and uncertainty of the operation process of the equipment, and the susceptibility of the monitoring signal to propagation paths, noise and other factors, the condition monitoring is difficult to achieve high efficiency and high accuracy, so that the equipment fault prediction and control are very difficult.

For example, the invention patent with application number CN201811651018.2 discloses an online fault detection system and method applied to an intelligent manufacturing workshop, which apply image acquisition and image processing technology to the product manufacturing defect information identification and diagnosis of the intelligent manufacturing workshop, and perform real-time image acquisition, processing and feature extraction on the product in the intelligent manufacturing workshop to obtain the feature vector representation of the product manufacturing information; the characteristic vectors are used as input layers and sent to a deep neural network unit, then a large number of typical manufacturing defects obtained in the manufacturing process of an actual intelligent manufacturing workshop are effectively utilized through a manufacturing defect prediction model constructed based on the deep neural network, and a standard sample image library formed by typical manufacturing defect images is trained and learned by combining image acquisition and image processing technologies, so that the deep neural network model can be used for identifying and classifying defects manufactured in real time, and more accurate reference information is provided for product manufacturing accuracy and manufacturing information collection and analysis of the intelligent manufacturing workshop.

However, due to the particularity of the spinning line, in the fault detection process for the entire spinning line, not only the fault that may occur in each process in the line but also the influence of the fault in each process on the entire spinning line need to be considered. The optimal effect of fault detection is that faults can be accurately predicted and analyzed in advance, so that fault coping processing can be timely performed, and the fault detection can be prevented. Therefore, the fault detection of the spinning production line is more complex and more difficult to realize than the fault detection of other equipment or parts, so that the characteristics of the intelligent spinning production line need to be combined to provide an effective way for the fault detection of the intelligent spinning production line.

Disclosure of Invention

Aiming at the defects in the prior art, the invention aims to provide an intelligent spinning production line fault detection system, which is used for accurately identifying the running state of a spinning production line finally by constructing a state identification sub-model and a state identification total model of each process of the spinning production line and training the models by using monitoring data of historical speed and vibration signals, so that the early warning is timely carried out on potential faults, and the influence of the production line faults on the spinning efficiency and quality is reduced.

In order to achieve the purpose, the invention adopts the following technical scheme:

an intelligent spinning production line fault detection system comprises a data acquisition module, a data processing module, a state identification module and a fault early warning module which are sequentially connected; wherein the content of the first and second substances,

the data acquisition module acquires running state signals of the spinning equipment through a sensor and sends the acquired state signals to the data processing module;

the data processing module carries out noise reduction processing on the running state signal and then extracts characteristic signal parameters which can reflect the running state of the spinning equipment most;

the state recognition module takes the characteristic signal parameters as input vectors, recognizes the running state of the spinning equipment by using a state recognition model, and sends corresponding state early warning indication signals to the fault early warning module;

and the fault early warning module sends out a corresponding fault early warning signal according to the state early warning indication signal.

Further, the data acquisition module comprises a blowing-carding data acquisition unit, a coarse data acquisition unit, a fine data acquisition unit and a spooling data acquisition unit; wherein the content of the first and second substances,

the blowing-carding data acquisition unit is used for acquiring running state signals of an opening blowing box, a cotton feeding box and a carding machine;

the coarse data collecting unit is used for collecting running state signals of the drawing frame and the roving frame;

the spun yarn data acquisition unit is used for acquiring running state signals of a spinning frame;

the winding data acquisition unit is used for acquiring running state signals of the winding machine.

Further, the state identification model comprises a state identification sub-model and a state identification total model, wherein the state identification sub-model comprises a blowing-carding state identification model, a rough state identification model, a spun yarn state identification model and a winding state identification model; wherein the content of the first and second substances,

the blowing-carding unit state identification model is used for identifying the operation states of the opening blowing box, the cotton feeding box and the carding machine according to the operation state signals collected by the blowing-carding unit data collection unit;

the rough state identification model is used for identifying the running states of the drawing frame and the roving frame according to the running state signals collected by the rough data collecting unit;

the spun yarn state identification model is used for identifying the running state of the spinning frame according to the running state signals collected by the spun yarn data collection unit;

the winding state identification model is used for identifying the operation state of the winding machine according to the operation state signals collected by the winding data collection unit;

and the state recognition total model is used for comprehensively recognizing the running state of the spinning production line according to the recognition result of the state recognition sub-model.

Further, the sensors include a speed sensor and a vibration sensor, and the operation state signal includes an operation speed signal and a vibration frequency signal.

Further, the data processing module carries out noise reduction processing on the vibration frequency signal and then extracts a characteristic vibration signal parameter which can most reflect the running state of the spinning equipment; the data processing module extracts an operation speed parameter from the operation speed signal; and the data processing module sends the characteristic vibration signal parameter and the running speed parameter to the state identification module together.

Further, the method for extracting the characteristic vibration signal parameter comprises the following steps:

(1) performing discrete wavelet transform on the vibration signals to obtain the wavelet coefficients of the vibration signals of all scales;

(2) performing noise reduction processing on the wavelet coefficients of the vibration signals of all scales by using a wavelet noise reduction technology to obtain the wavelet coefficients and scale coefficients of the vibration signals;

(3) obtaining a vibration signal characteristic scale parameter distributed along the scale according to the wavelet coefficient and the scale coefficient, and then constructing a vibration signal characteristic scale vector;

(4) extracting a vibration signal characteristic scale parameter which can best reflect the fault characteristic of the spinning equipment from the vibration signal characteristic scale vector to construct a characteristic vibration signal parameter vector;

the characteristic scale parameter of the vibration signal which can best reflect the fault characteristic of the spinning equipment is determined by the frequency of the vibration signal when the spinning equipment is in normal operation.

Further, the state recognition module takes the characteristic vibration signal parameter and the running speed parameter as input vectors, respectively recognizes the running states of corresponding spinning equipment by using the state recognition sub-models, comprehensively recognizes the running states of the spinning production line by using the state recognition total model, and then sends corresponding state early warning indication signals to the fault early warning module.

Further, the state identification submodel comprises a spinning equipment state distribution probability vector χ, a spinning equipment state transition probability matrix A, an observed value probability distribution U and a state retention time probability distribution V which are recorded as phi_r＝(χ_r，A_r，U_r，V_r) R is a positive integer of 1 to 4, phi₁、Φ₂、Φ₃And phi₄Respectively representing a blowing-carding state identification model, a coarse state identification model, a spun yarn state identification model and a spooling state identification model.

Further, the state recognition submodel firstly trains the model through the spinning equipment historical running state signals collected by the data collection module to obtain model parameters of the spinning equipment in each state; and then, determining the current operation state model parameters of the spinning equipment according to the current operation state signals of the spinning equipment acquired by the data acquisition module, and judging the current operation state of the spinning equipment according to the matching degree of the current model parameters of the spinning equipment and the model parameters of the spinning equipment in each state.

Further, the state recognition total model is used for obtaining the current operation state S of the spinning equipment in each process according to the state recognition sub-models_rThe operation state weight ω and the process relation R of each process determine the operation state of the spinning line, and are represented by ψ (S)_rω, R), R is a positive integer from 1 to 4.

Advantageous effects

Compared with the prior art, the intelligent spinning production line fault detection system provided by the invention has the following beneficial effects:

(1) the intelligent spinning production line fault detection system provided by the invention collects and processes the running speed and the vibration signal through the data collection module and the data processing module, and extracts the running speed parameter and the characteristic vibration signal parameter as the input vector of the state identification module; the state recognition sub-model recognizes the running state of each process according to the corresponding input vector, then recognizes the running state of the spinning production line through the state recognition total model, and sends out a corresponding state early warning signal through the state early warning module, so that the running state of the spinning production line can be accurately recognized, potential faults are timely early warned, and the influence of the production line faults on the spinning efficiency and quality is reduced.

(2) According to the invention, according to the variability of the running speed in the spinning production line, particularly in the thick yarn and thin yarn processes, the speed of the roller needs to be changed constantly according to the change of the cotton sliver quality, and the influence of the speed change on the vibration signal is eliminated by simultaneously acquiring the running speed signal and the vibration signal, so that the reliability of the input vector during state recognition is improved, and the accuracy of the state recognition is further improved.

(3) According to the frequency band to which the vibration frequency of the spinning equipment during normal operation belongs, the characteristic vibration signal parameter which can reflect the fault characteristics of the spinning equipment most is extracted, and the input vector of state recognition is simplified, so that the workload during state recognition is reduced, the state recognition efficiency is improved, and the state recognition accuracy is improved.

(4) The invention firstly identifies the running state of each procedure by constructing the state identification sub-model and the state identification total model, then determining the state operation model parameters of each process through the state identification submodel, determining the operation state of the whole spinning production line according to the operation state weight of each process and the association degree of each process, not only predicting the operation state of each process independently, but also predicting the operation state of the whole spinning production line, by bringing the influence of the state change of a single process on the state change of the whole spinning production line into the state recognition category, the comprehensiveness, reliability and effectiveness of the fault detection of the spinning production line are improved, by incorporating the operation state weight and the process correlation degree of each process into the state recognition total model, the fault continuity detection between the processes can be realized, and the fault predictability of the spinning production line is improved.

Drawings

FIG. 1 is a block diagram of a fault detection system of an intelligent spinning production line provided by the invention;

FIG. 2 is a flow chart of a method for extracting a characteristic vibration signal;

FIG. 3 is a flow chart of the intelligent spinning production line fault detection system provided by the invention;

FIG. 4 is a block diagram of the state recognition process of the intelligent spinning production line fault detection system provided by the invention.

Detailed Description

The technical solutions of the embodiments of the present invention will be described clearly and completely below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments; all other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without any inventive step, are within the scope of the present invention.

The invention provides a fault detection system of an intelligent spinning production line, which is mainly used for judging whether a potential fault exists in the intelligent spinning production line through state recognition of the intelligent spinning production line, so that the potential fault is early warned in time, and the influence of the production line fault on the spinning efficiency and the spinning quality is reduced.

According to the technological process and characteristics of the intelligent spinning production line, the spinning production line can be divided into four production procedures of carding, roving, spinning and spooling. Wherein the blowing and carding process mainly comprises a plucker, a cotton mixer, a cotton opener, a cotton cleaner, a cotton feeding box and a cotton carding machine; the roving process comprises a drawing frame and a roving frame, wherein the drawing frame and the roving frame both comprise drafting rollers; in the spinning process, a spinning frame is used for drafting and twisting the rough yarn; in the winding process, the bobbin yarn or skein yarn from the spinning process is processed into a bobbin meeting certain requirements on a winder through the winder. Each spinning apparatus generates a vibration signal during operation, and when the operation state of the apparatus changes, the vibration frequency fluctuates correspondingly.

The raw cotton is processed in sequence through each production process of the spinning production line to obtain fine yarns, and each process is influenced mutually in the process of independent operation. If the carding procedure is failed, the thickness of the obtained cotton sliver is uneven, the drawing effect is seriously influenced, and finally the spun yarn quality is unqualified.

Based on this, the invention provides an intelligent spinning production line fault detection system, please refer to fig. 1, which comprises a data acquisition module, a data processing module, a state identification module and a fault early warning module, which are connected in sequence.

The data acquisition module comprises a blowing-carding data acquisition unit, a drawing-carding data acquisition unit, a spun yarn data acquisition unit and a winding data acquisition unit, and each data acquisition unit acquires an operation state signal of the spinning equipment through a sensor and sends the acquired state signal to the data processing module.

The blowing-carding data acquisition unit is used for acquiring running state signals of an opening blowing box, a cotton feeding box and a carding machine; the coarse data acquisition unit is used for acquiring running state signals of the drawing frame and the roving frame; the spun yarn data acquisition unit is used for acquiring running state signals of the spinning frame; the winding data acquisition unit is used for acquiring running state signals of the winding machine.

Further, the data collecting unit collects an operation speed signal and a vibration frequency signal of the spinning device through a speed sensor and a vibration sensor.

The data processing module carries out noise reduction processing on the vibration frequency signal and then extracts characteristic vibration signal parameters which can reflect the running state of the spinning equipment most; the data processing module extracts an operation speed parameter from the operation speed signal; and the data processing module sends the characteristic vibration signal parameter and the running speed parameter to the state identification module together.

Referring to fig. 2, the characteristic vibration signal parameters are extracted by the following steps:

(1) performing discrete wavelet transform on the vibration signal to obtain wavelet coefficients of all scales, which is specifically as follows:

performing m-layer discrete wavelet decomposition and reconstruction on the vibration signal x (n) to obtain wavelet coefficients of all scalesAnd scale factor

The vibration signals respectively contain vibration information of different frequency bands from high frequency to low frequency of the vibration signals, and the visual estimation of local energy under different scales is embodied.

(2) Performing denoising treatment on the wavelet coefficient by using wavelet denoising technology to obtain a wavelet coefficient D with high signal-to-noise ratio_j＝{d_j(k) K 1,2, …, N, j 1,2, …, m } and a scaling factor C_m. To unify, C_mIs denoted by D_m+1Then D can be replaced_j＝{d_j(k) K is 1,2, …, N, j is 1,2, …, m, m +1} considered as a division of the vibration signal x (N).

(3) Obtaining a vibration signal characteristic scale parameter distributed along the scale according to the wavelet coefficient and the scale coefficient, and then constructing a vibration signal characteristic scale vector, wherein the specific steps are as follows:

obtaining wavelet coefficient D with high signal-to-noise ratio_j＝{d_j(k) Based on the formula (1) and the formula (2), a vibration signal characteristic scale parameter W of each scale is obtained from the formula (1) and the formula (2), and a vibration signal characteristic scale vector is constructed by using the vibration signal characteristic scale parameters of each scale as elements, and is represented as T, where T is [ W ]₁,W₂,…,W_m]；

Wherein, because the low-frequency reconstructed signal of the (m + 1) th layer can be regarded as a low-frequency gradient signal, the characteristic scale parameter of the vibration signal is small, and therefore, the W can be converted into the low-frequency gradient signal_(m+1)Neglected.

In the formula (d)_F(j)(k) Is d_(j)(k) J is 1,2, …, m, m +1, W_jThe j-th scale vibration signal characteristic scale parameter of the vibration signal x (n).

(4) And extracting the vibration signal characteristic scale parameters which can best reflect the fault characteristics of the spinning equipment from the vibration signal characteristic scale vectors to construct characteristic vibration signal parameter vectors.

The characteristic scale parameter of the vibration signal which can reflect the fault characteristics of the spinning equipment is determined by the vibration frequency of the spinning equipment during normal operation, and is specifically determined according to the vibration frequency of the spinning equipment corresponding to the collected vibration signal.

If the vibration frequency of the spinning equipment is in a high frequency range, selecting a vibration signal characteristic scale parameter of a high frequency range scale wavelet coefficient as a characteristic signal parameter vector; and if the vibration frequency of the spinning equipment is in a low-frequency range, selecting the vibration signal characteristic scale parameter of the low-frequency range scale wavelet coefficient as a characteristic signal parameter vector.

The state identification module is used for taking the characteristic vibration signal parameters and the running speed parameters as input vectors, recording the input vectors as E (t) ═ v (t), T (t)) and representing t moments, identifying the running state of the spinning equipment by using the state identification model, and sending corresponding state early warning indication signals to the fault early warning module.

The state recognition model comprises a state recognition sub-model and a state recognition total model, wherein the state recognition sub-model comprises an air-blowing-carding state recognition model, a coarse state recognition model, a spun yarn state recognition model and a spooling state recognition model.

Specifically, the blowing-carding unit state identification model is used for identifying the operation states of the opening and picking, the cotton feeding box and the carding machine according to the operation state signals collected by the blowing-carding unit data collection unit; the rough state identification model is used for identifying the running states of the drawing frame and the roving frame according to the running state signals collected by the rough data collecting unit; the spun yarn state identification model is used for identifying the running state of the spinning frame according to the running state signals collected by the spun yarn data collection unit; the winding state identification model is used for identifying the operation state of the winding machine according to the operation state signals collected by the winding data collection unit; and the state recognition total model is used for comprehensively recognizing the running state of the spinning production line according to the recognition result of the state recognition sub-model.

The state recognition module respectively recognizes the running states of corresponding spinning equipment by using the state recognition submodels, comprehensively recognizes the running states of the spinning production line by using the state recognition total model, and then sends corresponding state early warning indication signals to the fault early warning module, and the fault early warning module sends corresponding fault early warning signals according to the state early warning indication signals.

The state identification submodel comprises a spinning equipment state distribution probability vector chi, a spinning equipment state transition probability matrix A, an observed value probability distribution U and a state retention time probability distribution V which are recorded as phi_r＝(χ_r，A_r，U_r，V_r) R is a positive integer of 1 to 4, phi₁、Φ₂、Φ₃And phi₄Respectively representing a blowing-carding state identification model, a coarse state identification model, a spun yarn state identification model and a spooling state identification model.

Wherein the spinning equipment state distribution probability vector χ ═ (χ)₁,χ₂,…,χ_N) As shown in formula (3):

χ_i＝P(q₁＝h_i),1≤i≤N (3)

in the formula, N is the number of the operation states of the spinning device, and the state 1 represents the normal state, and the states 2 to N represent the states of the spinning device are gradually deteriorated, namely gradually close to the fault state; h is_iIs the i-th degradation state; q. q.s₁Represents the macroscopic state of the equipment at the initial moment 1; then x_iThe probability of the i-th operating state of the spinning device is shown. For example, in a normal state of one spinning line, χ ═ is (1,0, …, 0).

Spinning apparatus state transition probability matrix a ═ { a ═ a_ij}_N×NWherein a is_ijAs shown in formula (4):

a_ij＝P(q_t+1＝S_j|q_t＝S_i),1≤i,j≤N (4)

in the formula, S_iAnd S_jRepresenting the microscopic state of the device, q_t+1And q is_tRepresenting the macroscopic states of the device at times t +1 and t, respectively.

The observed value probability density function is shown in equation (5):

wherein, N (X, mu)_jk,∑_jk) Is a probability density function whose mean is mu_jkThe covariance matrix is ∑_jkK is the constituent U_j(X) number of mixed probability density functions, c_jkIs the weight of the kth component, satisfiesThe observed probability distribution U is as shown in equation (6):

U＝(U_j(X),j＝0,1,2,…,N) (6)

the state dwell time probability function is shown in equation (7):

V_i(d)＝P(d|q_t＝i),1≤i≤N,1≤d≤D (7)

in the formula, V_i(d) Representing the probability that the ith state lasts D time units, D being the maximum dwell time of the state, the state dwell time probability distribution V ═ V_i(d))_N×D。

When fault detection is carried out specifically, the state recognition submodel firstly trains the model through the spinning equipment historical running state signals collected by the data collection module to obtain model parameters of the spinning equipment in each state; and then, determining the current model parameters of the spinning equipment according to the current running state signals of the spinning equipment collected by the data collection module, judging the current running state of the spinning equipment according to the matching degree of the current model parameters of the spinning equipment and the model parameters of the spinning equipment in each state, and giving the current state grade of the spinning equipment.

Training the state recognition submodels by a method of repeated iteration and state reestimation, and specifically comprising the following steps:

(1) obtaining a model by dividing the vibration signal at equal intervals at the beginning of trainingInitial value of parameter phi_k0According to the state recognition sub-model formed by the initial values, segmenting input training data corresponding to each state;

(2) reestimating the probability distribution U of the observed value and the probability distribution V of the state staying time in the model to obtain the parameter values of the probability distribution U of the observed value and the probability distribution V of the state staying time;

(3) obtaining a set of new state identification sub-model initial values phi 'from the step (2)'_k0Phi 'will'_k0Reestimating the state recognition submodel as an initial value to obtain a model parameter phi ″_k0The obtained result is identical to the original initial value phi_k0Comparing, if the difference is smaller than a preset threshold value, indicating that the model parameters are converged, and not needing to perform reestimation calculation, and outputting a calculation result as a state identification sub-model parameter; otherwise, taking the calculation result as a new initial value to perform a new round of operation.

Further, the state recognition total model identifies the current operation state S of the spinning equipment in each process according to the state recognition sub-models_rThe operation state weight ω and the process relation R of each process determine the operation state of the spinning line, and are represented by ψ (S)_r，ω_r，R_r) And r is a positive integer from 1 to 4.

Wherein the weight ω of the operation state of each process_rRepresents the importance degree of each process operation in the spinning production line, and satisfies omega₁+ω₂+ω₃+ω₄1, calculated by equation (8):

in the formula (I), the compound is shown in the specification,

representing the operating state model parameters of the process r.

The relevance of each procedure is determined by the characteristics of the spinning production process and the relevance of each procedure, and the running state weight of each procedure and the relevance of each procedure are incorporated into the state recognition total model, so that the fault continuity detection of each procedure can be realized, and the fault predictability of the spinning production line is improved.

Referring to fig. 3 and 4, the fault detection method of the present invention includes the following steps:

s1, a data acquisition module respectively acquires running speed signals and vibration frequency signals of a blowing-carding process, a drawing-roughing process, a spinning process and a winding process, and transmits the acquired signals to a data processing module;

s2, the data processing module obtains an operation speed parameter and a characteristic vibration signal parameter through signal conversion processing and characteristic extraction, records the operation speed parameter and the characteristic vibration signal parameter as E (t) ═ v (t) and T (t)), and takes the operation speed parameter and the characteristic vibration signal parameter as an input vector of the state identification module;

s3, determining the current running state model parameters of the spinning equipment according to the blowing-carding-drawing state identification model, the combined-roughing state identification model, the spun yarn state identification model and the spooling state identification model respectively according to input vectors E1(t), E2(t), E3(t) and E4(t) of the blowing-carding-drawing process, the combined-roughing process, the spun yarn process and the spooling process

Judging the current operation state S of the spinning equipment according to the matching degree of the current model parameters of the spinning equipment and the model parameters of the spinning equipment in each state₁、S₂、S₃、S₄；

S4, the state recognition total model is used for recognizing the current running state S of the spinning equipment according to each process₁、S₂、S₃、S₄The operation state weight ω of each step and the correlation degree R of each step determine the operation state TS of the spinning line.

According to the invention, according to the variability of the running speed in the spinning production line, particularly in the thick yarn and thin yarn processes, the speed of the roller needs to be changed constantly according to the change of the cotton sliver quality, and the influence of the speed change on the vibration signal is eliminated by simultaneously acquiring the running speed signal and the vibration signal, so that the reliability of the input vector during state recognition is improved, and the accuracy of the state recognition is further improved. Then, through the state recognition sub-model and the state recognition total model, the running state of each process is recognized firstly, then the state running model parameters of each process are determined through the state recognition sub-model, and the running state of the whole spinning production line is determined according to the running state weight of each process and the relevance of each process, so that the running state of each process can be predicted independently, the running state of the whole spinning production line can be predicted, the influence of the state change of a single process on the state change of the whole spinning production line is brought into the state recognition category, and the comprehensiveness, the reliability and the effectiveness of the fault detection of the spinning production line are improved.

The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.