scholarly journals Research on the Fault Diagnosis Method of Mine Fan Based on Sound Signal Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shijie Song ◽  
Dandan Qiu ◽  
Sunwei Qin
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Frequency Component ◽  
Sound Signal ◽  
Normal Operation ◽  
Signal Acquisition ◽  
Noise Elimination ◽  
Denoising Method ◽  
Diagnosis Method ◽  
Fault State

The underground local fan and auxiliary fan also play a vital role in the underground air quality, compared with the system fan. However, the number of underground local fans and auxiliary fans is large and widely distributed, which is disadvantageous to adopt the same method of online monitoring and fault diagnosis method as the system fan. In order to find a new fault diagnosis method, which is cost-effective and reliable, this paper proposes a fault diagnosis method based on sound signal. It analyzes the source of fan noise and studies the overall scheme of mine fan fault diagnosis expert system based on sound signal. The fault expert system consists of four parts: signal acquisition and noise elimination, feature extraction, state recognition, and fault diagnosis. Its principle is briefly introduced. The denoising method of wavelet is adopted in this paper. Wavelet packet is used to extract the characteristics of sound signal, and the energy size and energy proportion of each frequency component are used as the basis of knowledge acquisition and reasoning. Through the analysis of the measured signals of the fan in the normal operating state, the feature vectors were extracted as the basis for the discrimination of the normal state after noise elimination. At the same time, the audio processing software was used to simulate the sound signals in three fault states. Then, the feature vector of the fault state is extracted, which is obviously different from that of the fan in the normal operation. As the basis of fault state analysis of the expert system, it lays the foundation for the realization of the expert system of mine fan equipment running state diagnosis.

Rotor Crack Detection Based on Multi-Vibration Signal Fusion Collected from the Basement of Machinery Using SVM and Statistical Characteristics Methodology

2010 ◽  
Vol 34-35 ◽  
pp. 1000-1004
Author(s):  
Xue Jun Li ◽  
K. Wang ◽  
Ling Li Jiang ◽  
T. Zhang
Keyword(s):  
Fault Diagnosis ◽  
Crack Detection ◽  
Feature Fusion ◽  
Vibration Signal ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Signal Acquisition ◽  
Multiple Sensors ◽  
Signal Fusion ◽  
Diagnosis Method

As the poor generability of special sensor support frame and the inconvenience of signal acquisition in the process of common fault diagnosis for cracked rotor, a new fault diagnosis method is presented in this paper. this method takes the basement of rotor test rig as the monitoring objects and makes feature fusion for time-domain statistics of multiple sensors using SVM (support vector machine). The result of experiment showed that the method using the multi-sensor signal fusion technology collected from the basement of machinery has the advantages of better diagnostic precision for rotor crack diagnosis, furthermore, it supplies a new way for rotor fault diagnosis.

Improved Power Transformer Winding Deformation Fault Diagnosis Method

2014 ◽  
Vol 666 ◽  
pp. 149-153 ◽  
Author(s):  
Hong Zhong Ma ◽  
Ning Jiang ◽  
Chun Ning Wang ◽  
Zhi Hui Geng
Keyword(s):  
Fault Diagnosis ◽  
Fundamental Frequency ◽  
Characteristic Frequency ◽  
Power Transformer ◽  
Vibration Signal ◽  
Frequency Component ◽  
Transformer Winding ◽  
Diagnosis Method ◽  
Winding Deformation ◽  
Harmonic Components

according to analysing the generation principle of transformer winding deformation and its impact on the vibration signal, and make a large number of trial, it can be found in addition to the fundamental frequency component that can reflect the failure, the new characteristic frequency which conclude 50Hz frequency component and some of its harmonic components, the harmonic components of the fundamental frequency can also reflect the failure. Transformer winding deformation fault diagnosis method is proposed based on the relationship between the characteristic frequency, it can not only diagnose whether the failure inside the transformer windings, but also determine the type of fault. In order to verify the proposed method, deformation fault is set to the actual transformer winding. After de-noising, discounted processing, the acquisition monitoring points of vibration signal is used by the proposed method, and the actual transformer is diagnosed, The diagnostic result is same with actual failure. It is shown that the proposed diagnostic method is accurate and feasible.

scholarly journals Research on rapier loom fault system based on cloud-side collaboration

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260888
Author(s):  
Yanjun Xiao ◽  
Kuan Wang ◽  
Weiling Liu ◽  
Kai Peng ◽  
Feng Wan
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Fault System ◽  
Human Machine Interaction ◽  
Diagnosis System ◽  
Depth Study ◽  
Diagnosis Method ◽  
Fault Diagnosis System ◽  
Electrical Faults ◽  
Machine Interaction

The electrical control system of rapier weaving machines is susceptible to various disturbances during operation and is prone to failures. This will seriously affect the production and a fault diagnosis system is needed to reduce this effect. However, the existing popular fault diagnosis systems and methods need to be improved due to the limitations of rapier weaving machine process and electrical characteristics. Based on this, this paper presents an in-depth study of rapier loom fault diagnosis system and proposes a rapier loom fault diagnosis method combining edge expert system and cloud-based rough set and Bayesian network. By analyzing the process and fault characteristics of rapier loom, the electrical faults of rapier loom are classified into common faults and other faults according to the frequency of occurrence. An expert system is built in the field for edge computing based on knowledge fault diagnosis experience to diagnose common loom faults and reduce the computing pressure in the cloud. Collect loom fault data in the cloud, train loom fault diagnosis algorithms to diagnose other faults, and handle other faults diagnosed by the expert system. The effectiveness of loom fault diagnosis is verified by on-site operation and remote monitoring of the loom human-machine interaction system. Technical examples are provided for the research of loom fault diagnosis system.

The Study of Vibration Diagnosis Method for GIS

2013 ◽  
Vol 295-298 ◽  
pp. 2429-2432
Author(s):  
Pei Ding ◽  
Zhen Hua Yan ◽  
Fei Yue Ma ◽  
Liang Zhang ◽  
Jun Hao Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Field Test ◽  
Detection Method ◽  
Detection System ◽  
Vibration Signal ◽  
Normal Operation ◽  
Vibration Test ◽  
Test Results ◽  
Vibration Detection ◽  
Diagnosis Method

Gas insulated switchgear (GIS) will generate vibration during normal operation for the electromagnetic force. There will be generate abnormal vibration when the contacts are undesirable, the guide rod stressed unevenness. Therefore, it can be effective in fault diagnosis of this machineries through the vibration test of GIS in field. The vibration detection method of GIS equipment in field is studied in this paper, the composition of the vibration detection system is described. The field test has been done using vibration detection system and the test results show that through the vibration signal detected from the GIS equipment, the vibration characteristics of GIS can be clarified and make the fault diagnosis effectively.

Spacecraft Fault Diagnosis Based on Telemetry Data Mining and Fault Tree Analysis and Design of Expert System

2013 ◽  
Vol 760-762 ◽  
pp. 1062-1066 ◽  
Author(s):  
Xiang Gao ◽  
Tao Zhang ◽  
Hong Jin Liu ◽  
Jian Gong
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Expert System ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Telemetry Data ◽  
Tree Analysis ◽  
Analysis And Design ◽  
Diagnosis Method ◽  
The Impact

In this paper, a fault diagnosis method for spacecraft based on telemetry data mining and fault tree analysis was proposed. Decision trees are constructed from the history telemetry data of the spacecraft, and are used to classify the current data which is unknown whether it is fault. If there is a fault, the fault tree method will be used to analyze the fault reason and the impact on the spacecraft system. This method can effectively solve the problem of diagnostic knowledge acquisition. We design and construct a fault diagnosis expert system for spacecraft based on this diagnosis method. An experiment is presented to prove the effectiveness and practicality of the expert system.

The Motor Rotor Fault Diagnosis Method Based on Linear Mixing Blind Separation Model

2014 ◽  
Vol 602-605 ◽  
pp. 2420-2425
Author(s):  
Xin Guo Hou ◽  
Fan Bu
Keyword(s):  
Fault Diagnosis ◽  
Frequency Resolution ◽  
Blind Separation ◽  
Stator Current ◽  
Diagnosis Method ◽  
Mixing Matrix ◽  
Fault State ◽  
Ica Algorithm ◽  
Fault Characteristic ◽  
Separation Model

The detection precision of fault diagnosis based on frequency spectral analysis of stator current is easily restricted by noise jamming and frequency resolution. A fault diagnosis method for induction motor based on linear mixing model is proposed to resolve this problem. The fault characteristic signals are separated from the motor stator current by Fast-ICA algorithm and its amplitude is calculated according to the estimated mixing matrix. The fault diagnosis is achieved by difference of the amplitude on the normal state and the fault state of the motor. In this paper, the fault diagnosis of the broken rotor bars faults is used as an example to explain the conclusion as mentioned. Experiment result shows that the broken-rotor-bar fault can be diagnosed by the algorithm with better effect on the condition of short data block.

scholarly journals A Novel Method for Clutch Pressure Sensor Fault Diagnosis

Vehicles ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 191-209 ◽  
Author(s):  
Zhichao Lv ◽  
Guangqiang Wu
Keyword(s):  
Fault Diagnosis ◽  
Pressure Sensor ◽  
Fault Tolerant ◽  
Operating Conditions ◽  
Safety And Reliability ◽  
Diagnosis Method ◽  
Novel Method ◽  
Constant Output ◽  
Fault State ◽  
Output Component

As a crucial output component, a clutch pressure sensor is of great importance on monitoring and controlling a whole transmission system and a whole vehicle status, both of which play important roles in the safety and reliability of a vehicle. With the help of fault diagnosis, the fault state prediction of a pressure sensor is realized, and this lays the foundation for further fault-tolerant control. In this paper, a fault diagnosis method of Dual Clutch Transmission (DCT) is designed. Firstly, a Variable Force Solenoid (VFS) valve model is established. A feed-forward input system is added to correct the first-order inertial link of the sensor on the second step. Finally, the parameters of the established system model are identified by using the measured data of the actual transmission and the Genetic Algorithm (GA). An identified model is then used for designing a fault observer. The constant output faults of 0, 3, and 5 V, pulse fault, and bias fault that enterprises are concerned with are selected to simulate and verify the fault observer under four different operating conditions. The results show that the designed fault observer has great fault diagnosis performance.

Research on fault diagnosis method of rapier loom based on the fusion of expert system and fault tree

2021 ◽  
pp. 1-13
Author(s):  
Yanjun Xiao ◽  
Furong Han ◽  
Yvheng Ding ◽  
Weiling Liu
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Knowledge Base ◽  
Fault Tree ◽  
Maintenance Cost ◽  
Intelligent Fault Diagnosis ◽  
Tree Model ◽  
Intelligent Diagnosis ◽  
Diagnosis Method ◽  
System Knowledge

The safety and stability of the rapier loom during operation directly impact the quality of the fabric. Therefore, it is of great significance to carry out fault diagnosis research on rapier looms. In order to solve the problems of low diagnosis efficiency, untimely diagnosis, and high maintenance cost of existing rapier looms in manual troubleshooting of loom failures. This paper proposes a new intelligent fault diagnosis method for rapier looms based on the fusion of expert system and fault tree. A new expert system knowledge base is formed by combining the dynamic fault tree model with the expert system knowledge base. It solves the problem that the traditional expert system cannot achieve precise positioning in the face of complex fault types. Construct the rapier loom’s fault diagnosis model, build the intelligent diagnosis platform, and finally realize the intelligent fault diagnosis of the rapier loom. Experimental results show that the algorithm can quickly diagnose and locate rapier loom faults. Compared with the current intelligent diagnosis algorithm, the algorithm structure is simplified, which provides a theoretical basis for the broad application of intelligent fault diagnosis on rapier looms.

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