Textile machinery fault positioning system

Abstract

The invention discloses a textile machinery fault positioning system, and belongs to the technical field of textile machinery. According to the invention, operation data of the textile machinery can be collected and analyzed in real time, potential faults can be found in time, fault types and positions can be accurately and timely identified through a feature extraction and pattern classification algorithm, the false alarm rate and the missing report rate are effectively reduced, and the system can give alarm or prompt information in time when abnormal conditions are found, so that the system is convenient to use. According to the textile machine, sufficient time is provided for workers to take measures for treatment, losses caused by fault shutdown are reduced, the operation efficiency and the equipment safety of the textile machine are improved, and the production efficiency and the product quality of textile enterprises are guaranteed.

Description

Technical Field

[0001] The invention relates to the technical field of textile machinery, in particular to a textile machinery fault locating system. Background Art

[0002] With the rapid development of the textile industry, the working reliability of textile machinery and equipment has become increasingly important. In order to prevent serious production stoppage accidents of textile machinery, reduce the failure rate of equipment and reduce equipment downtime, it is particularly important to monitor the working status of the equipment and judge faults. Summary of the invention

[0003] The purpose of the present invention is to provide a textile machinery fault location system to solve the problems raised in the above background technology.

[0004] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a textile machinery fault location system, comprising a signal acquisition module, a feature extraction module, a pattern classification module, and a decision module, wherein the signal acquisition module is used to acquire vibration, sound or other related signals in real time from the operation process of the textile machinery, the feature extraction module comprises a preprocessing module and a feature selection module, the preprocessing module preprocesses the acquired signal to improve the signal-to-noise ratio and readability of the signal, and prepares for extracting feature parameters reflecting the textile machinery fault to improve its accuracy, wherein the feature parameters comprise time domain feature parameters and frequency domain feature parameters;

[0005] As a further preferred embodiment of the present technical solution: the feature selection module selects the feature parameters that contribute most to fault identification by screening and optimizing a large number of extracted feature parameters, so as to improve the operating efficiency and accuracy of the system; the pattern classification module classifies the operating state of the textile machinery by using the extracted feature parameters, and can match the feature parameters with known operating states or fault modes according to preset algorithms and models, so as to determine whether the current operating state of the textile machinery is normal, or whether there is a certain fault;

[0006] As a further preferred embodiment of the present technical solution: the pattern classification module includes a state recognition module, and the state recognition module further determines whether the device is in a normal operating state or has an abnormality according to the classification result of the pattern classification module, and further identifies and determines the type and possible cause of the fault;

[0007] As a further preferred embodiment of the present technical solution: the state identification module includes a continuous monitoring module and a fault classification module, the continuous monitoring module: after the fault is identified and processed, continues to monitor the textile machinery to ensure its stable operation, and the fault classification module further classifies the detected abnormal signal to determine the type and location of the fault;

[0008] As a further preferred embodiment of the present technical solution: the fault classification module includes a new fault detection module and a dynamic fault identification module. The new fault detection module is to timely discover and detect new faults during the monitoring process to ensure the continued effectiveness of the system. The dynamic fault identification module can combine historical data and real-time data to dynamically identify faults, provide more accurate fault diagnosis information, and judge the severity and development trend of the faults.

[0009] As a further preferred embodiment of the present technical solution: the decision module makes a decision based on the result of the pattern classification. If it is judged to be a normal operating state, the system will continue to monitor. If it is judged to be an abnormal or faulty state, the system will trigger the process of fault classification and dynamic fault identification, and give corresponding alarms or prompt information so that the staff can take timely measures to deal with it;

[0010] As a further preferred embodiment of the present technical solution: the preprocessing module further includes a signal enhancement function, which is used to enhance the acquired signal to improve the signal quality and the accuracy of the analysis;

[0011] As a further preferred embodiment of the present technical solution: the fault classification module adopts a machine learning or deep learning algorithm, which can continuously optimize the classification model according to historical data and real-time data, thereby improving the accuracy and generalization ability of fault classification;

[0012] As a further preferred embodiment of the present technical solution: the system also includes a user interface module for displaying real-time monitoring data, fault alarm information, fault diagnosis results and system configuration information, and supporting users to perform remote monitoring and configuration.

[0013] Compared with the prior art, the beneficial effects of the present invention are: the present invention can collect and analyze the operating data of textile machinery in real time, discover potential faults in time, and through feature extraction and pattern classification algorithms, can accurately and timely identify the type and location of faults, effectively reducing the false alarm rate and missed alarm rate, and the system can give an alarm or prompt information in time when an abnormal situation is found, providing staff with sufficient time to take measures to deal with it, reducing losses caused by downtime due to faults, improving the operating efficiency and equipment safety of textile machinery, and ensuring the production efficiency and product quality of textile enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The present invention is a flowchart of a textile machinery fault locating system. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] Example

[0017] See also Figure 1 As shown, the present invention provides a technical solution: a textile machinery fault location system, including a signal acquisition module, a feature extraction module, a pattern classification module, and a decision module. The signal acquisition module is used to acquire vibration, sound or other related signals in real time from the operation process of the textile machinery. The feature extraction module includes a preprocessing module and a feature selection module. The preprocessing module preprocesses the acquired signal to improve the signal-to-noise ratio and readability of the signal, and prepares for extracting characteristic parameters reflecting the textile machinery fault to improve its accuracy. The characteristic parameters include time domain characteristic parameters and frequency domain characteristic parameters.

[0018] In this embodiment, specifically: the feature selection module selects the feature parameters that contribute most to fault identification by screening and optimizing a large number of extracted feature parameters to improve the operating efficiency and accuracy of the system; the pattern classification module uses the extracted feature parameters to classify the operating status of the textile machinery, and can match the feature parameters with known operating status or fault modes according to preset algorithms and models, so as to determine whether the current operating status of the textile machinery is normal, or whether there is some kind of fault.

[0019] In this embodiment, specifically: the pattern classification module includes a state identification module, and the state identification module further determines whether the equipment is in normal operation or has an abnormality according to the classification result of the pattern classification module, and performs further identification to determine the type of fault and possible causes.

[0020] In this embodiment, specifically: the state identification module includes a continued monitoring module and a fault classification module. The continued monitoring module: after the fault is identified and processed, continues to monitor the textile machinery to ensure its stable operation. The fault classification module further classifies the detected abnormal signals to determine the type and location of the fault.

[0021] In this embodiment, specifically: the fault classification module includes a new fault detection module and a dynamic fault identification module. The new fault detection module ensures the continued effectiveness of the system by timely discovering and detecting new faults during the monitoring process. The dynamic fault identification module can combine historical data and real-time data to dynamically identify faults, provide more accurate fault diagnosis information, and judge the severity and development trend of the fault.

[0022] In this embodiment, specifically: the decision module makes a decision based on the result of pattern classification. If it is judged to be a normal operating state, the system will continue to monitor. If it is judged to be an abnormal or fault state, the system will trigger the fault classification and dynamic fault identification process, and give corresponding alarms or prompt information so that the staff can take timely measures to deal with it.

[0023] In this embodiment, specifically: the preprocessing module also includes a signal enhancement function, which is used to enhance the acquired signal to improve the signal quality and the accuracy of analysis.

[0024] In this embodiment, specifically: the system also includes a user interface module for displaying real-time monitoring data, fault alarm information, fault diagnosis results and system configuration information, and supporting users to perform remote monitoring and configuration.

[0025] Working principle or structural principle: When the equipment is working, the signal acquisition module obtains the operating signal from the textile machinery, and then the preprocessing module performs preliminary processing on the acquired signal. Then the feature extraction module extracts the characteristic parameters that can reflect the operating status of the textile machinery. After that, the pattern classification module uses the characteristic parameters to classify the operating status. Finally, the decision module makes a decision based on the classification result and gives corresponding alarm or prompt information.

[0026] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A textile machinery fault location system, comprising a signal acquisition module, a feature extraction module, a pattern classification module, and a decision module, characterized in that: The signal acquisition module is used to acquire vibration, sound or other related signals in real time from the operation process of the textile machinery. The feature extraction module includes a preprocessing module and a feature selection module. The preprocessing module preprocesses the acquired signal to improve the signal-to-noise ratio and readability of the signal, and prepares for extracting characteristic parameters reflecting textile machinery failures to improve their accuracy. The characteristic parameters include time domain characteristic parameters and frequency domain characteristic parameters.

2. A textile machinery fault location system according to claim 1, characterized in that: The feature selection module selects the feature parameters that contribute most to fault identification by screening and optimizing a large number of extracted feature parameters to improve the operating efficiency and accuracy of the system. The pattern classification module uses the extracted feature parameters to classify the operating status of the textile machinery, and can match the feature parameters with known operating status or fault modes according to preset algorithms and models to determine whether the current operating status of the textile machinery is normal or whether there is a certain fault.

3. A textile machinery fault location system according to claim 1, characterized in that: The mode classification module includes a state identification module, which further determines whether the equipment is in normal operation or abnormality according to the classification result of the mode classification module, and further identifies and determines the type and possible cause of the fault.

4. A textile machinery fault location system according to claim 3, characterized in that: The state identification module includes a continuous monitoring module and a fault classification module. The continuous monitoring module continues to monitor the textile machinery after the fault is identified and processed to ensure its stable operation. The fault classification module further classifies the detected abnormal signals to determine the type and location of the fault.

5. A textile machinery fault location system according to claim 4, characterized in that: The fault classification module includes a new fault detection module and a dynamic fault identification module. The new fault detection module ensures the continued effectiveness of the system by timely discovering and detecting new faults during the monitoring process. The dynamic fault identification module can combine historical data and real-time data to dynamically identify faults, provide more accurate fault diagnosis information, and judge the severity and development trend of the fault.

6. A textile machinery fault location system according to claim 1, characterized in that: The decision module makes a decision based on the results of pattern classification. If it is judged to be a normal operating state, the system will continue to monitor. If it is judged to be an abnormal or faulty state, the system will trigger the process of fault classification and dynamic fault identification, and give corresponding alarms or prompt information so that the staff can take timely measures to deal with it.

7. A textile machinery fault location system according to claim 1, characterized in that: The preprocessing module also includes a signal enhancement function for performing enhancement processing on the acquired signal to improve the signal quality and the accuracy of analysis.

8. A textile machinery fault location system according to claim 5, characterized in that: The fault classification module adopts machine learning or deep learning algorithms, which can continuously optimize the classification model according to historical data and real-time data, and improve the accuracy and generalization ability of fault classification.

9. A textile machinery fault location system according to claim 1, characterized in that: The system also includes a user interface module for displaying real-time monitoring data, fault alarm information, fault diagnosis results and system configuration information, and supporting users to perform remote monitoring and configuration.

Images