Fault Diagnosis and Separation for a Distributed Rotary-laser Scanning System

The wMPS is a laser-based measurement system used for large scale metrology.However,it is susceptible to external factors such as vibrations,which can lead to unreliable measurements.This paper presents a fault diagnosis and separation method which can counter this problem.To begin with,the paper uses simple models to explain the fault diagnosis and separation methods.These methods are then mathematically derived using statistical analysis and the principles of the wMPS.A comprehensive solution for fault diagnosis and separation is proposed,considering the characteristics of the wMPS.The effectiveness of this solution is verified through experimental observations.It can be concluded that this approach can detect and separate false observations,thereby enhancing the reliability of the wMPS.

Fault separation and detection algorithm based on Mason Young Tracy decomposition and Gaussian mixture models

Purpose-For the large-scale power grid monitoring system equipment,its working environment is increasingly complex and the probability of fault or failure of the monitoring system is gradually increasing.This paper proposes a fault classification algorithm based on Gaussian mixture model(GMM),which can complete the automatic classification of fault and the elimination of fault sources in the monitoring system.Design/methodology/approach-The algorithm first defines the GMM and obtains the detection value of the fault classification through a method based on the causal Mason Young Tracy(MYT)decomposition under each normal distribution in the GMM.Then,the weight value of GMM is used to calculate weighted classification value of fault detection and separation,and by comparing the actual control limits with the classification result of GMM,the fault classification results are obtained.Findings-The experiment on the defined non-thermostatic continuous stirred-tank reactor model shows that the algorithm proposed in this paper is superior to the traditional algorithm based on the causal MYT decomposition in fault detection and fault separation.Originality/value-The proposed algorithm fundamentally solves the problem of fault detection and fault separation in large-scale systems and provides support for troubleshooting and identifying fault sources.

Intrinsic component filtering for fault diagnosis of rotating machinery

Fault diagnosis of rotating machinery has always drawn wide attention.In this paper,Intrinsic Component Filtering(ICF),which achieves population sparsity and lifetime consistency using two constraints:l1=2 norm of column features and l3=2-norm of row features,is proposed for the machinery fault diagnosis.ICF can be used as a feature learning algorithm,and the learned features can be fed into the classification to achieve the automatic fault classification.ICF can also be used as a filter training method to extract and separate weak fault components from the noise signals without any prior experience.Simulated and experimental signals of bearing fault are used to validate the performance of ICF.The results confirm that ICF performs superior in three fault diagnosis fields including intelligent fault diagnosis,weak signature detection and compound fault separation.