Rotating Machine Fault Diagnosis Research Articles

Rotating machine fault diagnosis using dimension reduction with linear local tangent space alignment

A novel fault diagnosis method using dimension reduction with linear local tangent space alignment is proposed in this paper. With this method, the mixed-domain feature set is first constructed to completely characterize the property of each fault by combining Empirical Mode Decomposition (EMD) with the Autoregression (AR) model coefficients. Then, Linear Local Tangent Space Alignment (LLTSA) is used to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination. By using the tangent space in the neighborhood of a data point to represent the local geometry, and aligning those local tangent spaces in the low-dimensional space (which is linearly mapped from the raw high-dimensional space), LLTSA can not only gain a perfect approximation of low-dimensional intrinsic geometric structure within the high-dimensional observation data, but can also enhance local within-class relations. Finally, the Littlewoods-Paley wavelet support vector machine (LPWSVM) is proposed to perform fault classification with the obtained low-dimensional eigenvectors. Compared with the existing methods, the proposed approach has improved the fault diagnosis precision. The experiments on deep groove ball bearings fault diagnosis demonstrated the advantage and effectiveness of the proposed fault diagnosis approach.

Wavelets for fault diagnosis of rotary machines: A review with applications

Over the last 20 years, particularly in last 10 years, great progress has been made in the theory and applications of wavelets and many publications have been seen in the field of fault diagnosis. This paper attempts to provide a review on recent applications of the wavelets with focus on rotary machine fault diagnosis. After brief introduction of the theoretical background on both classical wavelet transform and second generation wavelet transform, applications of wavelets in rotary machine fault diagnosis are summarized according to the following categories: continuous wavelet transform-based fault diagnosis, discrete wavelet transform-based fault diagnosis, wavelet packet transform-based fault diagnosis, and second generation wavelet transform-based fault diagnosis. In addition, some new research trends, including wavelet finite element method, dual-tree complex wavelet transform, wavelet function selection, new wavelet function design, and multi-wavelets that advance the development of wavelet-based fault diagnosis are also discussed.

De-Noising Method of Acoustic Emission Signal for Rolling Bearing Based on Adaptive Wavelet Correlation Analysis

In acoustic emission (AE) detection technique, to avoid the serious noise disturbance in the fault diagnosis of rotary machine, a de-noising method based on adaptive wavelet correlation analysis to be applied to the AE signal is proposed. First, AE signals are decomposed by dyadic wavelet transform and at the same time the AE signal is divided into available coefficients and noise coefficients. Secondly, the available coefficients are reconstructed to restore the original real signal after de-noising process. Finally, the de-noising threshold is set by adaptive threshold method based on wavelet entropy. On the simulation of AE signal and the bearing fault measured AE signal using wavelet entropy correlation de-noising method, the traditional wavelet de-noising method and the traditional lifting wavelet de-noising method three kinds of de-noising methods are compared, the results show that the wavelet entropy correlation de-noising method can greatly improve the rolling bearing AE signal de-noising effect.

Multiscale noise tuning of stochastic resonance for enhanced fault diagnosis in rotating machines

The interference from background noise makes it difficult to identify incipient faults of a rotating machine via vibration analysis. By the aid of stochastic resonance (SR), the unavoidable noise can, however, be applied to enhance the signal-to-noise ratio (SNR) of a system output. The classical SR phenomenon requires small parameters, which is not suited for rotating machine fault diagnosis as the defect-induced fault characteristic frequency is usually much higher than 1Hz. This paper investigates an improved SR approach with parameter tuning for identifying the defect-induced rotating machine faults. A new method of multiscale noise tuning is developed to realize the SR at a fixed noise level by transforming the noise at multiple scales to be distributed in an approximate 1/f form. The proposed SR approach overcomes the limitation of small parameter requirement of the classical SR, and takes advantage of the multiscale noise for an improved SR performance. Thus the method is well-suited for enhancement of rotating machine fault identification when the noise is present at different scales. A new scheme of rotating machine fault diagnosis is hence proposed based on the SR with multiscale noise tuning and has been verified by means of practical vibration signals carrying fault information from bearings and a gearbox. An enhanced performance of the proposed fault diagnosis method is confirmed as compared to several traditional methods.

Development of a Master-Slave Architecture Handheld Rotating Machine Fault Diagnosis Instrument

To insure that sampling signal integrity, accuracy and real-time performance can adapt to the development of rotating machine fault diagnosis technology, a master-slave architecture handheld rotating machine fault diagnosis instrument was developed based on S3C2410 ARM IC and TMS320VC5509A DSP IC. It provided an effective method for the field monitoring and diagnosis of the large rotating machine. The whole design idea and the structure of the hardware and the software were systematically introduced. The paper focused on the master-slave architecture design of the hardware, the communication methods between the master and the slave processor, and the signal pretreatment module design. Put into practice, the practicability, reliability and stability of the instrument were confirmed.

The Development of Embedded Rotating Machine Fault Diagnosis System Based on Qt/Embedded

As the constantly growth of embedded Technology, embedded products put into use in almost everywhere around our life. This paper mainly introduce how to set up the Qt/Embedded Cross-compiling environment in Linux system platform, and the development and transplant of embedded fault diagnosis system use development tools of Qt/Embedded. This system achieve those functions, including fault display, signal analysis, diagnostic model training and data storage or transmittal, by graphical interfaces designing and performance function addition. And finally create the application document which applies to ARM framework embedded processor by Cross-compiling. In the end, the application document is transplanted and run in the target platform, and testing the diagnosis effects.

Determination of sample size using power analysis and optimum bin size of histogram features

Vibration signals are used in fault diagnosis of rotary machines as a source of information. Lots of work have been reported on identification of faults in roller bearing by using many techniques. Of late, application of machine learning approach in fault diagnosis is gaining momentum. Machine learning approach consists of chain of activities like, data acquisition, feature extraction, feature selection and feature classification. While histogram features are used, there are still a few questions to be answered such as how many histogram bins are to be used to extract features and how many samples to be used to train the classifier. This paper provides a mathematical study to choose the bin size and the minimum sample size to train the classifier using power analysis with statistical stability. A typical bearing fault diagnosis problem is taken as a case for illustration and the results are compared with that of entropy based algorithm (J48) for determining minimum sample size and bin size.

Transient modeling and parameter identification based on wavelet and correlation filtering for rotating machine fault diagnosis

At constant rotating speed, localized faults in rotating machine tend to result in periodic shocks and thus arouse periodic transients in the vibration signal. The transient feature analysis has always been a crucial problem for localized fault detection, and the key aim for transient feature analysis is to identify the model and its parameters (frequency, damping ratio and time index) of the transient, and the time interval, i.e. period, between transients. Based on wavelet and correlation filtering, a technique incorporating transient modeling and parameter identification is proposed for rotating machine fault feature detection. With the proposed method, both parameters of a single transient and the period between transients can be identified from the vibration signal, and localized faults can be detected based on the parameters, especially the period. First, a simulation signal is used to test the performance of the proposed method. Then the method is applied to the vibration signals of different types of bearings with localized faults in the outer race, the inner race and the rolling element, respectively, and all the results show that the period between transients, representing the localized fault characteristic, is successfully detected. The method is also utilized in gearbox fault diagnosis and the effectiveness is verified through identifying the parameters of the transient model and the period. Moreover, it can be drawn that for bearing fault detection, the single-side wavelet model is more suitable than double-side one, while the double-side model for gearbox fault detection. This research proposed an effective method of localized fault detection for rotating machine fault diagnosis through transient modeling and parameter detection.

Research on Fault Diagnosis of Ship Stern Bearing Based on Vibration Analysis

The application of vibration analysis in fault diagnosis of rotary machine becomes more and more popular. Aiming at an abnormal high frequency vibration of a stern bearing, this paper proposed a method of fault diagnosis based on the analysis of vibration characteristics and the spectral analysis. From the gathered vibration data of a stern shaft bench through Pulse Vibration Analysis Platform, using the speed spectrum array to determine the characteristics of vibration source, with the analysis of vibration characteristics of the bench, it concluded that the abnormal high frequency vibration source of the stern bearing is a weak rigidity part of bench─ tacho-torque meter. The vibration data of again test after strengthening the part illustrated the validity of this diagnostic mode.

Improved Wavelet Neural Network Based on Hybrid Genetic Algorithm Applicationin on Fault Diagnosis of Railway Rolling Bearing

The method of improved wavelet transform neural network based on hybrid GA(genetic algorithm) is presented to diagnose rolling bearings faults in this paper. Genetic Artificial Neural Networks(GA-ANN) overcomes BP neural network’s fault of slow convergence, long hours of training, and falling into the local minimum point. And First, the signal is processed through the wavelet deoising, Then, three— layer wavelet packet is adopted to decompose the denoising signal of rolling beatings, and constructs the wavelet packet energy eigenvector, then takes those wavelet packet energy eigenvectors as fault samples to train BP neural network. Genetic algorithm is used to optimize the training process of BP network. The experimental results show that the optimized BP network by genetic algorithm can diagnose bearing faults, and it is superior to the BP network without optimization, the method has fair prospects of application for the rotary machine fault diagnosis.

Observations of acoustic emission in a hydrodynamic bearing

Purpose – Numerous studies have been conducted in the field of acoustic emission (AE) technology applied to rotating machine fault diagnosis. Principally, most of the work to date has been focused on correlating AE activity to the defect condition on rolling element bearings with limited investigations on hydrodynamic bearings. This paper aims to address this issue.Design/methodology/approach – This paper presents experimental tests aimed at understanding the influence of speed and load on generation of AE in a hydrodynamic bearing.Findings – The research presented shows that the power losses associated which hydrodynamic bearing has a direct influence on the generation of AE.Research limitations/implications – Further research should include the application of the proposed methodology on a broad range of operational machines employing hydrodynamic bearings.Practical implications – In developing the AE technology for monitoring hydrodynamic bearings operated under variable speed and load conditions, it is...

Rotating machine fault diagnosis using empirical mode decomposition

In this paper, an empirical mode decomposition (EMD) based approach for rotating machine fault diagnosis is investigated. EMD is a new time–frequency analyzing method for nonlinear and non-stationary signals. By using EMD a complicated signal can be decomposed into a number of intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. The IMFs, working as the basis functions, represent the intrinsic oscillation modes embedded in the signal. However, our research shows that IMFs sometimes fail to reveal the signal characteristics due to the effect of noises. Hence, combined mode function (CMF) is presented. With CMF, the neighboring IMFs are combined to obtain an oscillation mode depicting signal features more precisely. The adaptive filtering features of EMD and CMF are discussed, and the simulation signals are applied to test their performance. Finally, a practical fault signal of a power generator from a thermal-electric plant is analyzed to diagnose the fault by using EMD and CMF. The results show that EMD and CHF can extract the rotating machine fault characteristics and identify the fault patterns effectively.

Fault Diagnosis System for Rotary Machine Based on Fuzzy Neural Networks

This paper is concerned with the application of fuzzy neural networks to fault diagnosis systems for rotary machines. In practical fault diagnosis, it is very difficult to improve the recognition rate of pattern recognition, especially when the sample data are similar. To solve these difficulties, a fault diagnosis system using fuzzy neural networks is proposed in this research. A fault diagnosis system with fuzzy neural networks is based on a series of standard fault pattern pairings between fault symptoms and fault. Fuzzy neural networks are trained to memorize these standard pattern pairs. Unlike other neural networks, fuzzy neural networks adopt bi-directional association. They make use of information from both the fault symptoms and the fault patterns, which can improve recognition rate greatly. When an unknown sample becomes the input for a trained fault diagnosis system, the fault diagnosis system can make fault diagnosis by bi-directional association of fuzzy neural networks. Through experiments with a rotor testing table and applications in monitoring and fault diagnosis of water pump sets of oil plant, it is verified that fuzzy neural networks have a well distinguished ability and are effective to perform fault diagnosis of rotary machines.