Equipment Fault Diagnosis Research Articles

Fault pattern recognition of gas blower based on discrete Fourier transform interpolation algorithm

The mechanical equipment fault spectrum often contains random noise, and traditional spectrum estimation methods introduce large errors in the fault diagnosis of mechanical equipment including gas blower. Accurate frequency estimation of multi-frequency signals that are contaminated is a common problem. To address the fault spectral characteristics of gas blowers, a novel discrete Fourier transform interpolation algorithm is presented based on traditional practice. The algorithm combines the zero-complement technique and the main lobe fitting technique. The accuracy of the spectrum correction was improved greatly with the new algorithm, and it could effectively promote fault pattern recognition. Application examples and simulation results showed that the new algorithm is very successful in the fault pattern recognition of gas blowers. Under noise conditions, the proposed algorithm has a smaller estimation error than conventional algorithms and could achieve high precision, strong compatibility, and robustness. Theoretically, the algorithm has attained a higher level, and its application in engineering practice is also quite impressive due to its highly expected values.

EMD技术在船舶液压设备故障诊断中的应用研究

本文在分析传统液压设备故障诊断技术及信号处理方法在船舶液压系统故障诊断方面存在不足的基础上，论述了振动分析在船舶液压系统故障诊断领域应用的可行性及前景，分析了经验模态分解(EMD)技术在处理非平稳信号方面的工程应用优势；分别以经验模态分解算法和径向基函数网络为基础，结合船舶液压系统应用实际，构建了船舶液压系统振动信号特征向量提取模型和故障诊断模型；最后以某型船艇装备液压舵机系统实测数据为基础，对构建的特征向量提取模型和故障诊断模型进行了验证。 Based on the analysis of the shortcomings of the traditional hydraulic equipment fault diagnosis technology and signal processing signal processing methods in the field of marine hydraulic system fault diagnosis, the feasibility and prospect of vibration analysis method in the field of marine hy-draulic system fault diagnosis are discussed, the advantages of EMD in dealing with non-stationary signals are analyzed. On the basis of EMD and RBF network, combined with the application of ma-rine hydraulic system, the vibration signal characteristic vector extraction model and fault diagno-sis model of marine hydraulic system are constructed. Finally, the model of characteristic vector extraction and fault diagnosis model are validated on the basis of measured data of hydraulic steering gear equipped with a certain type of watercraft.

Enhanced Orthogonal Matching Pursuit Algorithm and Its Application in Mechanical Equipment Fault Diagnosis

The vibration signal measured from the mechanical equipment is associated with the operation of key structure, such as the rolling bearing and gear. The effective signal processing method for early weak fault has attracted much attention and it is of vital importance in mechanical fault monitoring and diagnosis. The recently proposed atomic sparse decomposition algorithm is performed around overcomplete dictionary instead of the traditional signal analysis method using orthogonal basis operator. This algorithm has been proved to be effective in extracting useful components from complex signal by reducing influence of background noises. In this paper, an improved linear frequency-modulated (Ilfm) function as an atom is employed in the proposed enhanced orthogonal matching pursuit (EOMP) algorithm. Then, quantum genetic algorithm (QGA) with the OMP algorithm is integrated since the QGA can quickly obtain the global optimal solution of multiple parameters for rapidly and accurately extracting fault characteristic information from the vibration signal. The proposed method in this paper is superior to the traditional OMP algorithm in terms of accuracy and reducing the computation time through analyzing the simulation data and real world data. The experimental results based on the application of gear and bearing fault diagnosis indicate that it is more effective than traditional method in extracting fault characteristic information.

Guest Editorial

Guest Editorial

Fault Diagnosis of Coal Equipment Based on Dynamic Fuzzy Neural Network and BP Neural Network

Fault Diagnosis of Coal Equipment Based on Dynamic Fuzzy Neural Network and BP Neural Network

Research and Design of the Tobacco Machinery Equipment Remote Fault Diagnosis System Platform

Because of the machine equipment fault has the fuzzy characteristics, based on the ZJ19E monitoring system, proposed one kind based on the Internet of things technology and fuzzy neural network intelligent fault diagnosis of remote control system scheme, established the fault diagnosis model based on fuzzy neural network, realizes the remote diagnosis of the machine equipment fault, improves the traditional control methods greatly improved productivity and automation, and improves the high rate of fault diagnosis of the machine equipment, makes the ZJ19E monitoring system remotable and intelligentialize.

Evaluation of electrical insulation in three-phase induction motors and classification of failures using neural networks

This paper presents a study for the evaluation of the electrical insulation of the stator of three-phase induction motors (IM) and the classification of the failure mechanism using an approach based on computational intelligence tools (CIT). A brief review showing the main parameters for the evaluation of insulation condition and testing of IMs is presented, as well as the promising use of CITs for fault diagnosis of industrial equipment, including motors. This paper proposes a new methodology for evaluation and classification of insulation conditions with the aid of K-means clustering and of a classifier based on ANNs (artificial neural networks).

Fault Diagnosis of Coal Mine Equipment Based on Improved GA Optimized BP Neural Network

In the face of more and more faults in coal mine equipment, this paper presents the method of combining genetic algorithm (GA) and BP neural network to predict the failure. According to genetic algorithm has a very slow convergence speed, easy to fall into local optimum, this paper uses chaos and reverse individual learning initialization, followed by the use of differential algorithm to operate on the optimal individual. Finally, the improved fitness function is applied to the selection operation, and the accuracy of operation is improved by mutation probability and crossover probability. The improved algorithm is applied to the BP neural network to improve the training effect. The simulation results show that the proposed algorithm improves the accuracy and stability compared to the traditional BP neural network.

Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach

Reliable and quick response fault diagnosis is crucial for the wind turbine generator system (WTGS) to avoid unplanned interruption and to reduce the maintenance cost. However, the conditional data generated from WTGS operating in a tough environment is always dynamical and high-dimensional. To address these challenges, we propose a new fault diagnosis scheme which is composed of multiple extreme learning machines (ELM) in a hierarchical structure, where a forwarding list of ELM layers is concatenated and each of them is processed independently for its corresponding role. The framework enables both representational feature learning and fault classification. The multi-layered ELM based representational learning covers functions including data preprocessing, feature extraction and dimension reduction. An ELM based autoencoder is trained to generate a hidden layer output weight matrix, which is then used to transform the input dataset into a new feature representation. Compared with the traditional feature extraction methods which may empirically wipe off some “insignificant’ feature information that in fact conveys certain undiscovered important knowledge, the introduced representational learning method could overcome the loss of information content. The computed output weight matrix projects the high dimensional input vector into a compressed and orthogonally weighted distribution. The last single layer of ELM is applied for fault classification. Unlike the greedy layer wise learning method adopted in back propagation based deep learning (DL), the proposed framework does not need iterative fine-tuning of parameters. To evaluate its experimental performance, comparison tests are carried out on a wind turbine generator simulator. The results show that the proposed diagnostic framework achieves the best performance among the compared approaches in terms of accuracy and efficiency in multiple faults detection of wind turbines.

Time-Domain Fault Diagnosis Method of Mechanical and Electrical Equipment Based Improved Dynamic Time Wraping

Dynamic time warping used in speech recognition widely was migrated to fault feature extraction and diagnosis in time domain. Integration of phase compensation, slope weighted, derivative, sliding window connection, fast dynamic time planning method is applied to dynamic time warping method. And a new method of time-domain signal feature extraction and fault diagnostic based on improved dynamic time warping method of mechanical and electrical equipment was proposed. Identification and localization of fault signal characteristics may be done by improving dynamic time warping method to obtain a residual signal sequences with fault characterized sidebands and selecting the statistical characteristic parameters such as peak, RMS, kurtosis spectrum to complete identification and localization of fault signal characteristics. New time-domain fault trend prediction method of mechanical and electrical equipment was established based on new statistical parameter Thikat. A new idea and target was provided for fault diagnosis of mechanical and electrical equipment.

Calculated rate constants of the chemical reactions involving the main byproducts SO2F, SOF2, SO2F2 of SF6 decomposition in power equipment

SF6 is widely used in electrical equipment as an insulating gas. In the presence of an electric arc, partial discharge (PD) or spark, SF6 dissociation products (such as SF2, SF3 and SF4) react with the unavoidable gas impurities (such as water vapor and oxygen), electrodes and surrounding solid insulation materials, forming several toxic and corrosive byproducts. The main stable decomposition products are SO2F, SO2F2 and SOF2, which have been confirmed experimentally to have a direct relationship with discharge faults, and are thus expected to be useful in the fault diagnosis of power equipment. Various studies have been performed of the main SF6 decomposition species and their concentrations under different types of faults. However, most of the experiments focused on the qualitative analysis of the relationship between the stable products and discharge faults. Although some theoretical research on the formation of main SF6 derivatives have been carried out using chemical kinetics models, the basic data (chemical reactions and their rate constants) adopted in the model are inaccurate and incomplete. The complex chemical reactions of SF6 with the impurities are ignored in most cases. The rate constants of some reactions obtained at ambient temperature or in a narrow temperature range are adopted in the models over a far greater range, for example up to 12 000 K, due to the difficulty in the experimental measurement and theoretical estimation of rate coefficients, particularly at high temperatures. Therefore, improved theoretical models require not only the consideration of additional SF6 decomposition reactions in the presence of impurities but also on improved values of rate constants. This paper is devoted to determining the rate constants of the chemical reactions relating to the main byproducts of SF6 decomposition in SF6 gas-insulated power equipment: SO2F, SOF2 and SO2F2. Quantum chemistry calculations with density functional theory, conventional transition state theory and Wigner’s tunneling effect correction are employed to estimate the rate constants of four important chemical reactions: F + SO2F → SO2F2, F2 + SO2 → SO2F2, SO2F + SF5 → SF6 + SO2 and SOF3 + SF3 → SF4 + SOF2. The results are derived for a large temperature range, from 300 to 12 000 K, and finally fitted by a three-parameter Arrhenius equation. This work lays a basis for the further study of the SF6 decomposition mechanism by means of chemical kinetics modelling.

An Integrated Data Preprocessing Framework Based on Apache Spark for Fault Diagnosis of Power Grid Equipment

Big data techniques have been applied to power grid for the prediction and evaluation of grid conditions. However, the raw data quality can rarely meet the requirement of precise data analytics sin...

Datasocket Technology And Its Application In Equipment Remote Condition Monitoring And Fault Diagnosis

Abstract Virtual Instrument technology, based on DataSocket, is the product combined with computer technology, network communication technology and instrumentation technology. The paper has described some concepts about virtual instrument and LabVIEW. Now concerning the characteristic of equipment in grassroots units that equipment have diverse models and could be widespread distributed, and it could cause increasing difficulty of repair and maintenance about equipment. Thus, the paper proposed Virtual Instrument technology based on DataSocket and its application in equipment remote condition monitoring and fault diagnosis. DataSocket’s properties, structure and working principle were analyzed at the same time. By developing a set of equipment remote status monitoring and fault diagnosis system, the paper has tried to verify that the use of this technology can monitor real-time data and fault diagnosis

First Order Statistical Features Thermal Images for Surge Arrester Fault Classification

— Thermal imaging technique is a very convenient, versatile and non-contact method which has been used for fault condition diagnosis of electrical equipment. The fault condition diagnosis is composed with data acquisition, data pre-processing, data analysis and decision making. Some important features contain in thermal image can be extracted for equipment condition monitoring and fault diagnosis. This paper attempts to extract some important features from the zinc oxide (ZnO) surge arrester using first order statistical histogram extraction to classify the fault condition using neural network. The experimental work was carried out to capture thermal image of 120 kV rated ZnO surge arrester. The thermal images were segmented and plotted histogram using dedicated software. Some features such as the maximum, minimum, mean, standard deviation, and variance were extracted using the extraction method, classification of aging was carried out using the neural network based on the leakage current values. The health states consist of normal, defection and faulty. The results show that the thermal image features extracted using the extraction method can be used to classify the fault condition of the ZnO surge arresters

Research on Mechanical Fault Diagnosis Scheme Based on Improved Wavelet Total Variation Denoising

Wavelet analysis is a powerful tool for signal processing and mechanical equipment fault diagnosis due to the advantages of multiresolution analysis and excellent local characteristics in time-frequency domain. Wavelet total variation (WATV) was recently developed based on the traditional wavelet analysis method, which combines the advantages of wavelet-domain sparsity and total variation (TV) regularization. In order to guarantee the sparsity and the convexity of the total objective function, nonconvex penalty function is chosen as a new wavelet penalty function in WATV. The actual noise reduction effect of WATV method largely depends on the estimation of the noise signal variance. In this paper, an improved wavelet total variation (IWATV) denoising method was introduced. The local variance analysis on wavelet coefficients obtained from the wavelet decomposition of noisy signals is employed to estimate the noise variance so as to provide a scientific evaluation index. Through the analysis of the numerical simulation signal and real-word failure data, the results demonstrated that the IWATV method has obvious advantages over the traditional wavelet threshold denoising and total variation denoising method in the mechanical fault diagnose.

一种液晶面板设备故障诊断专家系统

一种液晶面板设备故障诊断专家系统

WSNs-Based Mechanical Equipment State Monitoring and Fault Diagnosis in China

Wireless sensor networks (WSNs) have been used in the state monitoring and fault diagnosis (SMFD) of mechanical equipment as a new signal collection and transmission technology, which have attracted a lot of attention recently. By applying the WSNs to the SMFD, many problems existing in conventional wired monitoring are solved. This paper attempts to summarize and review the recent researches and developments of the SMFD in mechanical equipment based on WSNs, providing comprehensive references for researchers concerned about this topic and helping them identify further research topics. Firstly, constraints in the conventional wired monitoring and the superiority of using WSNs in fault detection and diagnosis of mechanical equipment are illustrated. The development process of the SMFD in mechanical equipment is briefly introduced. Then, the recent WSNs applications to the SMFD of mechanical equipment in China are reviewed and summarized in terms of the classifications, such as monitored parameters, monitored objects, and data fusion methods. Finally, the potential future research directions are identified. WSNs are the effective technology to overcome the current bottleneck of the application in the SMFD of mechanical equipment.

Wireless Sensors System for Broken Rotor bar Fault Monitoring using Wavelet Analysis

Accurate condition monitoring prevents unexpected failures in electrical systems including induction machines, and hence improves their performance significantly. To enhance the reliability of condition monitoring systems, wireless sensor systems are developed. In the recent years, researchers have placed considerable emphasis on developing cost-effective scheme using wireless sensor systems for fault diagnosis of equipments in industry. As broken rotor bar is one of the main causes of malfunction in electrical motors, this paper proposes a method for early detection of this failure in induction machines using wireless sensor system. In this respect, a test bed is developed where a sensor measures the motor current and then a microcontroller connected to this current sensor read and send the data to wireless sensor for remote real time data analysis. In the receiver unit, a Lab VIEW based program is developed to store data in a database and MATLAB is used for signal processing and fault.

Extension Model of Fault Diagnosis for Complex Mechanical Equipment and Its Application

Based on the diversity, complexity and uncertainty of the fault information of complex mechanical equipment, this paper conducts extension analysis about the failure diagnosis for complex mechanical equipment and provides the extension model. This model carries out knowledge modeling about the failure diagnosis information based on matter element model, acquires the weight of different kinds of fault by grey correlation analysis, and establishes extensible dependence between different features of failure diagnosis according to extension data. Analysis of different fault information is conducted by identifying the type of faults based on extensible dependence. At last, the model and algorithm are testified through design examples.

The Diaphragm Pump Spindle Fault Diagnosis using HHT Based on EMD

The running performance of reciprocating piston diaphragm pump spindle affects reciprocating diaphragm pump operating conditions directly, even the effective operation of the entire pipeline transportation systems. Because of the diversity and uncertainty of the diaphragm pump spindle fault, this paper proposes a fault diagnosis method of HHT based on EMD. Firstly, it can process the original signal vibration data by EMD to get some IMF, then analyze each IMF through Hilbert transform to get HHT marginal spectrum, finally study the HHT marginal spectrum of IMF to obtain equipment fault diagnosis and research. The experimental results show that the method of HHT based on EMD is effec- tive and feasible for fault diagnosis of reciprocating piston diaphragm pump spindle.