Method For Multiple Fault Diagnosis Research Articles

Research on engine multiple fault diagnosis method based on cascade model

The engine is the core component of the power system, and the health status of the components of the engine is very important for the normal operation of the power system. Most of the exist-ing fault diagnosis methods diagnose the engine fault type without further analysis of the sever-ity of the fault. Different fault severity requires different maintenance measures. Therefore, this paper proposes a cascading fault diagnosis model based on Gated Recurrent Unit (GRU) to di-agnose the fault type and the severity of the corresponding fault type.Firstly, the effective fea-tures are extracted from the vibration signals, and then the features are input into the GRU for fault type diagnosis to obtain the sub-fault diagnosis model. After training, one fault type diag-nosis model and four fault severity diagnosis models are obtained. Then the obtained model is cascaded to obtain the total fault diagnosis network. Fault type diagnosis is located at the first level, and four fault severity diagnosis is located at the second level. The effectiveness of the proposed method is verified by experimental data

Multiple fault diagnosis for rolling bearings method employing CEEMD-GCN based on horizontal visibility graph

Multiple faults often occur in the operation of rotating machinery transmission systems. The fault signals of multiple bearings interfere with each other, which makes feature extraction and diagnosis of complex compound fault signals difficult. Because the graph convolution networks (GCN) can effectively map the structural information from complex data and its model has a certain generalization ability, this paper proposes a multiple fault diagnosis method for rolling bearings employing complete ensemble empirical mode decomposition (CEEMD) and a GCN (CEEMD-GCN) based on a horizontal visibility graph (HVG). Firstly, in order to highlight the effective feature information in the multiple fault signal and reduce noise interference, multiple indicators of correlation and kurtosis are used to reconstruct the decomposed signals through CEEMD; secondly, the reconstructed signals are constructed as an HVG, and the HVG maps the time series signal to the graphic structure data, reflecting the local geometric characteristics of the vibration signal through the horizontal visibility relationship; finally, taking the signal samples obtained by the HVG algorithm as the input data of the model, the GCN model is trained to realize the diagnosis of multiple faults. The experimental results show that the presented methodology is superior to other methods and exhibits generalization ability for multiple fault diagnosis.

MFCC based ensemble learning method for multiple fault diagnosis of roller bearing

MFCC based ensemble learning method for multiple fault diagnosis of roller bearing

Interval fuzzy probability method for power transformer multiple fault diagnosis

Accurately diagnosing power transformer faults is critical to improving the operational reliability of power systems. Although some researchers have made great efforts to improve the accuracy of transformer fault diagnosis, accurate diagnosis of multiple faults is still a difficult problem. In order to improve the accuracy of transformer multiple faults diagnosis, a multiple fault diagnosis method based on interval fuzzy probability is proposed. Different from the previous methods which provide single-value probability, this method use probability interval to represent the occurrence degree of various possible faults, which can objectively predict the potential faults that occurring in a transformer and provide a more reasonable explanation for the diagnosis results. In the proposed method, the interval fuzzy set is used to describe the evaluation of state variables and the interval fuzzy probability model based on interval weighted average is applied to integrate the fault information. The representative matrix of fault types based on fuzzy preference relationship is established to estimate the relative importance of each gas in the dissolved gases. The proposed method can provide the probability of probable faults in transformer, help engineers quickly determine the type and location of faults, and improve the accuracy of diagnosis and maintenance efficiency of transformer. The effectiveness of the method is verified with case studies.

Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM

Rolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibration signals of rolling bearings are often nonlinear and nonstationary, resulting in difficulty for feature extraction and fault recognition. In this paper, a hybrid method for multiple fault diagnosis of rolling bearings is presented. The bearing vibration signals are decomposed with the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to denoise and extract nonlinear entropy features. The nonlinear entropy features are further processed to select the more discriminative fault features and to reduce feature dimension. Then a multi-class intelligent recognition model based on ensemble support vector machine (ESVM) is constructed to diagnose different bearing fault modes as well as fault severities. The effectiveness of the proposed method is assessed via experimental case studies of rolling bearings under multiple operational conditions (i.e., speeds and loads). The results show that our method gives better diagnosis results as compared to some existing approaches.

Application of hybrid dimensionality reduction for fault diagnosis of three-phase inverter in PMSM drive system

The feature dimension reduction is a useful pre-processing step for fault diagnosis, where the irrelevant and redundant information in the data can be reduced. In this case, not only the computation complexity can be reduced, but also the better classification performance can be obtained. Hence, this paper presents a multiple fault diagnosis method for the three-phase inverter in PMSM drive system, where the hybrid dimensionality reduction method is applied to reduce the dimension of the fault features. First, time-domain fault features are extracted from the line-to-line voltage signals. Second, a feature reduction is performed by combining principal component analysis and linear discriminant analysis, where the linear discriminant analysis is improved by introducing the singular value decomposition and redefining the between-class scatter which solve the issue of small sample and the problem that the similar classes are not easily separated. Finally, the faults are diagnosed by support vector machine. Both the simulation and the experimental results show that the proposed method can improve the discrimination performance of the fault types after the dimension reduction, making it in favor of fault classification.

Dependency Model-Based Multiple Fault Diagnosis Using Knowledge of Test Result and Fault Prior Probability

Aiming at solving the multiple fault diagnosis problem as well as the sequence of all the potential multiple faults simultaneously, a new multiple fault diagnosis method based on the dependency model method as well as the knowledge in test results and the prior probability of each fault type is proposed. Firstly, the dependency model of the system can be built and used to formulate the fault-test dependency matrix. Then, the dependency matrix is simplified according to the knowledge in the test results of the system. After that, the logic ‘OR’ operation is performed on the feature vectors of the fault status in the simplified dependency matrix to formulate the multiple fault dependency matrix. Finally, fault diagnosis is based on the multiple fault dependency matrix and the ranking of each fault type calculated basing on the prior probability of each fault status. An illustrative numerical example and a case study are presented to verify the effectiveness and superiority of the proposed method.

An Improved KFCM Clustering Method Used for Multiple Fault Diagnosis of Analog Circuits

This study presents a new method for multiple fault diagnosis in moderate-sized analog circuits. Based on this method, a classifier is independently designed for each of the circuit components. Each of these classifiers only classifies the defect modes associated with the related component. The resultant effect is the much lower number of fault classes in each classifier than all of the circuit faults. Classifiers are designed based on a two-stage clustering method. Firstly, whole data are clustered by using Kmeans algorithm. Then, samples in each cluster are classified using a new version of KFCM algorithm. In this algorithm, initial cluster centers, as well as their number, are estimated by using an efficient method. Comparison with a neural network shows its very lower accuracy in classifying large number multiple faults. However, the proposed technique is capable of diagnosing multiple faults with acceptable accuracy (more than 91% for 15 single faults and more than 60% for most of the other multiple faults with the number greater than 200). Also this method has higher accuracy than traditional KFCM and YKFCM algorithms and this superiority rises along with increase in the number of faults.

基于改进Hu不变矩和SVM的转子故障诊断方法

在转子发生故障时，对转子进行准确地故障诊断一直是工程领域研究的重点。针对这一问题，本文提出了一种基于改进Hu不变矩和支持向量机(SVM)相结合的转子多故障诊断方法。该方法首先通过仿真合成4种常见故障的轴心轨迹图，并利用改进Hu不变矩分别对其进行特征提取，然后将得到的Hu不变矩值作为特征向量输入有向无环图SVM进行识别分类，最终实现转子的多故障诊断。经实验表明，该方法能够准确地完成转子的多故障诊断，同时通过与原始Hu不变矩比较，证明改进Hu不变矩在识别准确率上明显优于原始Hu不变矩。 The accurate diagnosis of fault conditions of steam rotor when faults occur in rotor has been the re-search focus in the field of engineering. To solve this problem, this study proposes a multiple fault diagnosis method for steam rotor based on the improved Hu invariant moments and SVM. First, 4 kinds of common faults’ Shaft orbits are synthesized by simulation in this method, and extract fea-tures by the improved Hu invariant moments, then, the Hu invariant moment values will input to the directed acyclic graph SVM as feature vectors to recognize and classify, and ultimately achieve the multiple fault diagnosis of rotor. The experimental results show that this method can accurately complete rotor multiple fault diagnosis. Meanwhile, to compared with the original Hu invariant moments, it is proved that the improved Hu invariant moments are better than the original Hu in-variant moments in the recognition accuracy.

Analysis and Application of Three Methods of Fault Diagnosis Problem

The problem of fault diagnosis of steam turbine used three methods of generating units, the results show that not only the method in this paper is multiple fault diagnosis method, and the formula is more simple, more practical application results.

Contribution to the Fault Tolerance for Hybrid Control Systems with Multiple Faults

Many diagnosis approaches are based in the assumption of single faults. This assumption may result to erroneous diagnosis statement in case where multiple faults occurs. Thereby multiple fault diagnosis is a challenging task especially in the control of large scale complex systems that can be viewed as hybrid systems. This owed to the fact that multiple faults are hard to detect because there consequences can mask or compensate to each other. The goal is to detect multiple faults as early as possible and provide a timely warning. A key issue is to prevent local faults to be developed into system failures that may cause safety hazards, stop temporarily the production and possible detrimental environment impact. This can be achieved by fault tolerant witch means that despites the faults occurrences the system is able to recover its original task with the same or degraded performance. Fault tolerance can be considered that it is constituted by two basic tasks, fault diagnosis and control redesign. In our work we addressed mainly to the first task introducing a method for multiple fault diagnosis in hybrid systems, while we propose a framework for fault tolerant hybrid control systems, which allows retaining acceptable performance under systems faults. The method is tested via a simple application to an electric power transmission system presented in our previous work.

Multiple Fault Diagnosis Method in Multistation Assembly Processes Using Orthogonal Diagonalization Analysis

Dimensional control has a significant impact on overall product quality and performance of large and complex multistation assembly systems. To date, the identification of process-related faults that cause large variations of key product characteristics (KPCs) remains one of the most critical research topics in dimensional control. This paper proposes a new approach for multiple fault diagnosis in a multistation assembly process by integrating multivariate statistical analysis with engineering models. The proposed method is based on the following steps: (i) modeling of fault patterns obtained using state space representation of process and product information that explicitly represents the relationship between process-related error sources denoted by key control characteristics (KCCs) and KPCs, and (ii) orthogonal diagonalization of measurement data using principal component analysis (PCA) to project measurement data onto the axes of an affine space formed by the predetermined fault patterns. Orthogonal diagonalization allows estimating the statistical significance of the root cause of the identified fault. A case study of fault diagnosis for a multistation assembly process illustrates and validates the proposed methodology.

A method for fault diagnosis in linear electronic circuits

A method for fault location and parameter identification in linear AC and DC circuits with limited accessible terminals for excitation and measurement is developed in this paper. Fault location is based on a derived relationship having a general meaning. It requires analyses of the circuit with nominal parameters and distinct excitations as well as measurements of some node voltages in the circuit with perturbed parameters. The fault parameter is identified using a formula obtained on the basis of the Woodbury expression. A decomposition technique is suggested enabling us to apply the method for multiple fault diagnosis. Several numerical examples illustrate the proposed approach and show its effectiveness. Copyright © 2000 John Wiley & Sons, Ltd.

Multiple fault diagnosis in combinational circuits using sensitizing input‐pairs

AbstractThis paper presents a new method for multiple fault diagnosis of combinational circuits using sensitizing input‐pairs. A partition of a circuit under test into subtree circuits and a generation method for diagnostic test are described. The set of diagnostic tests used in this paper is one of sensitizing input‐pairs that generate sensitizing paths including checkpoints on them.By studying the relation between a sensitizing path generated by a sensitizing input‐pair and a subtree circuit, a method is presented for multiple fault diagnosis in the subtree circuit based on the fault‐free and the faulty responses observed at primary outputs. A deduction algorithm is described for a value at an output of a subtree circuit which does not have a primary output. The proposed method is applied to benchmark circuits having double faults, triple faults, and fourfold faults. Experimental results show that suspected faults are identified within 8 to 30 percent of all stuck‐at 0 and 1 faults on all lines in the circuit.

Geometric approach for fault diagnosis in linear dynamic control systems

A method for multiple fault diagnosis of plants described by a linear difference model is proposed in the form of an algorithm. The algorithm is based on the observation that, when the number of faulty units of the system is known, the set of faulty units can be differentiated from other sets by checking linear varieties in the measurement data space. It is further shown that the system with t number of faults can be diagnosed within (t+1) sample/time units if the input-output measurements are rich and that the algorithm can be used for diagnosis even when the number of faults is not known in advance.