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Vibration Condition Monitoring Techniques for Fault Diagnosis of Electromotor with 1.5 Kw Power  [PDF]
H. Mohamadi Monavar,H. Ahmadi,S.S. Mohtasebi,S. Hasani
Journal of Applied Sciences , 2008,
Abstract: Vibration analysis is the main conditions monitoring techniques for machinery maintenance and fault diagnosis. This technique has its unique advantages and disadvantages associated with the monitoring and fault diagnosis of machinery. When this technique is conducted independently, only a portion of machine faults are typically diagnosed. However, practical experience has shown that this technique in a machine condition monitoring program provides useful reliable information, bringing significant cost benefits to industry. The objective of this research is to investigate the correlation between vibration analysis and fault diagnosis. This was achieved by vibration analysis and investigating different operating conditions of an experimental electromotor. The electromotor was initially run under normal operating conditions as a comparative test. A series of tests were then conducted corresponding to different operating condition. Our varieties were speed of electromotor at three levels, respectively 500, 1000 and 1500 rpm. We did three faults in our electromotor; there were misalignment, looseness and bad bearing. We coupled our electromotor to the variable blade fan and applied several load on that by changing the number of blade of fan. We have chosen 2, 6 and 10 blades fan to apply three different loads on our electromotor. Vibration data was regularly collected. Numerical data produced by vibration analysis were compared with vibration spectra in normal condition of healthy machine, in order to quantify the effectiveness of the vibration condition monitoring technique. The results from this paper have given more understanding on the dependent roles of vibration analysis in predicting and diagnosing machine faults.
Bearing fault detection with application to PHM Data Challenge
Pavle Bo?koski,Anton Urevc
International Journal of Prognostics and Health Management , 2011,
Abstract: Mechanical faults in production lines can result in partial or total breakdown of a production line, destruction of equipment and even catastrophes. Implementation of an adequate fault detection system represents an important step towards early detection of such faults, thus reducing the risk of unexpected failures. Traditionally, fault detection process is done by comparing the observed machine state with a set of historical data representing the fault--free state. However, such historical data are rarely available. In such cases, the fault detection process is performed by examining whether a particular pre--modeled fault signature can be matched within the signals acquired from the monitored machine. In this paper we propuse a solution to a problem of fault detection without any prior data, presented at PHM'09 Data Challenge. The solution is based on a two step algorithm. The first step, based on the spectral kurtosis method, is used to determine whether a particular experimental run is likely to contain a faulty element. In case of a positive decision, fault isolation procedure is applied as the second step. The fault isolation procedure was based on envelope analysis of filtered vibration signals. The filtering of the vibration signals was performed in the frequency band that maximizes the spectral kurtosis. The effectiveness of the proposed approach was evaluated for bearing fault detection, on the vibration data obtained from the PHM'09 Data Challenge.
Predictive Condition Monitoring of Induction Motor Bearing Using Fuzzy Logic
Prof. Rakeshkumar A. Patel
International Journal of Engineering Innovations and Research , 2012,
Abstract: Induction motor is critical component in industrial processes and is frequently integrated in commercially available equipment. Safety, reliability, efficiency and performance are the major concerns of induction motor applications. Due to high reliability requirements and cost of breakdown, condition monitoring, diagnosis and Protection increasing importance. Protection of an induction motor (IM) against possible problems, such as stator faults, rotor faults and mechanical faults, occurring in the course of its operation is very important, because it is very popular in industries. Bearing fault is well known mechanical fault of IM.41 0faults related to bearing in IM. To avoid break down of IM condition monitoring of motor bearing condition is very important during the normal operation. Various classical and AI techniques like fuzzy logic, neural network, neuro-fuzzy are used for condition monitoring and diagnosis of IM. Among the above mentioned AI techniques, Fuzzy logic is the best technique for condition monitoring and diagnosis of IM bearing condition. Therefore, the present paper focuses on fuzzy logic technique. In this paper Fuzzy logic is design for the condition monitoring and diagnosis of induction motor bearing condition using motor current and speed. After applying Fuzzy logic it has been seen that continuous monitoring of the current and speed values of the motor conditioned monitoring and diagnosis of induction motor bearing condition can be done.
Bearing fault diagnosis based on spectrum images of vibration signals  [PDF]
Wei Li,Mingquan Qiu,Zhencai Zhu,Bo Wu,Gongbo Zhou
Computer Science , 2015,
Abstract: Bearing fault diagnosis has been a challenge in the monitoring activities of rotating machinery, and it's receiving more and more attention. The conventional fault diagnosis methods usually extract features from the waveforms or spectrums of vibration signals in order to realize fault classification. In this paper, a novel feature in the form of images is presented, namely the spectrum images of vibration signals. The spectrum images are simply obtained by doing fast Fourier transformation. Such images are processed with two-dimensional principal component analysis (2DPCA) to reduce the dimensions, and then a minimum distance method is applied to classify the faults of bearings. The effectiveness of the proposed method is verified with experimental data.
Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis  [PDF]
Jinde Zheng,Junsheng Cheng,Yu Yang
Shock and Vibration , 2014, DOI: 10.1155/2014/154291
Abstract: A new rolling bearing fault diagnosis approach based on multiscale permutation entropy (MPE), Laplacian score (LS), and support vector machines (SVMs) is proposed in this paper. Permutation entropy (PE) was recently proposed and defined to measure the randomicity and detect dynamical changes of time series. However, for the complexity of mechanical systems, the randomicity and dynamic changes of the vibration signal will exist in different scales. Thus, the definition of MPE is introduced and employed to extract the nonlinear fault characteristics from the bearing vibration signal in different scales. Besides, the SVM is utilized to accomplish the fault feature classification to fulfill diagnostic procedure automatically. Meanwhile, in order to avoid a high dimension of features, the Laplacian score (LS) is used to refine the feature vector by ranking the features according to their importance and correlations with the main fault information. Finally, the rolling bearing fault diagnosis method based on MPE, LS, and SVM is proposed and applied to the experimental data. The experimental data analysis results indicate that the proposed method could identify the fault categories effectively. 1. Introduction The vibration signals of mechanical systems, especially for ones with fault, often show mutation, nonlinearity, and nonstationarity because of the strike, velocity chopping, structure transmutation, loading, and friction. Hence, it is very crucial for mechanical fault diagnosis to extract the fault feature information from the nonlinear and nonstationary signal. A primary method for dealing with the nonlinear and nonstationary signal is time-frequency analysis [1], which has been applied to the mechanical fault diagnosis field widely for its ability to provide local information both in time and frequency domains of vibration signals [2]. However, the time-frequency analysis method, such as wavelet transform or Hilbert-Huang transform [3, 4], which decomposes the vibration signal into several stationary monocomponent signals, cannot reflect the subtle dynamic changes of vibration signal effectively and, therefore, inevitably will have some limitations [5]. With the development of nonlinear dynamic theories, especially in recent years, a number of nonlinear parameters and methods, such as chaos theory, fractal dimension, and information entropy, have been applied to machine condition monitoring and fault diagnosis. For instance, Logan and Mathew elaborated the application of the correlation dimension to vibration fault diagnosis of rolling element bearing
International Journal of Engineering Science and Technology , 2011,
Abstract: Artificial Intelligence (AI) is an emerging technology. Research in AI is focused on developing computational approaches to intelligent behavior. The computer programs with which AI could be associated are primarily processes associated with complexity, ambiguity, ndecisiveness, and uncertainty. This present paper surveys the development of a condition monitoring procedure for different types ofbearings, which involves an artificial intelligence method as well as reviewed in order to examine the capability of AI methods and techniques to effectively address various hard-to-solve design tasks and issues relating different types of bearing fault. Although this review cannot be collectively exhaustive, it may be considered as a valuable guide for researchers who are interested in the domain of AI and wish to explore the opportunities offered by fuzzy logic, artificial neural networks and genetic algorithms for further improvement of conditioning monitoring for different types of bearing under different operating conditioning. Recent trends in research on conditioning monitoring using AI for different bearing have also been included.
An Approach to Fault Diagnosis of Rotating Machinery Using the Second-Order Statistical Features of Thermal Images and Simplified Fuzzy ARTMAP  [PDF]
Faisal Al Thobiani, Van Tung Tran, Tiedo Tinga
Engineering (ENG) , 2017, DOI: 10.4236/eng.2017.96033
Abstract: Thermal image, or thermogram, becomes a new type of signal for machine condition monitoring and fault diagnosis due to the capability to display real-time temperature distribution and possibility to indicate the machine’s operating condition through its temperature. In this paper, an investigation of using the second-order statistical features of thermogram in association with minimum redundancy maximum relevance (mRMR) feature selection and simplified fuzzy ARTMAP (SFAM) classification is conducted for rotating machinery fault diagnosis. The thermograms of different machine conditions are firstly preprocessed for improving the image contrast, removing noise, and cropping to obtain the regions of interest (ROIs). Then, an enhanced algorithm based on bi-dimensional empirical mode decomposition is implemented to further increase the quality of ROIs before the second-order statistical features are extracted from their gray-level co-occurrence matrix (GLCM). The highly relevant features to the machine condition are selected from the total feature set by mRMR and are fed into SFAM to accomplish the fault diagnosis. In order to verify this investigation, the thermograms acquired from different conditions of a fault simulator including normal, misalignment, faulty bearing, and mass unbalance are used. This investigation also provides a comparative study of SFAM and other traditional methods such as back-propagation and probabilistic neural networks. The results show that the second-order statistical features used in this framework can provide a plausible accuracy in fault diagnosis of rotating machinery.
Condition Monitoring and Fault Diagnosis of Serial Wound Starter Motor with Learning Vector Quantization Network
R. Bayir
Journal of Applied Sciences , 2008,
Abstract: In this study, a Graphical User Interface (GUI) software for real time condition monitoring and fault diagnosis of serial wound starter motors has been developed using Learning Vector Quantization (LVQ) neural network. The starter motors are serial wound dc motors which enable the Internal Combustion Engine (ICE) to run. When the starter motor fault occurs, the ICE cannot be run. Therefore, condition monitoring and pre-diagnosis of starter motor faults are important. The information of voltages and currents is acquired from the starter motor via data acquisition card and transferred to the program. With this program using LVQ network, six faults observed in the starter motors were successfully detected and diagnosed in real time. The GUI software makes it possible to condition monitoring and diagnose the faults in starter motors before they occur by keeping fault records of the past occurrences. This system can be used in service shops and in test departments of starter motor manufacturers. In addition, this system has potential to be used for real time condition monitoring and fault diagnosis of vehicles with the help of industrial computers.
Electro-pump Fault Diagnosis of Marine Ship by Vibration Condition Monitoring  [cached]
Payman Salami
Research Journal of Applied Sciences, Engineering and Technology , 2010,
Abstract: The objective of this research is to investigate the correlation between vibration analysis and fault diagnosis. This was achieved by vibration analysis of an electro-pump of marine ship. The vibration analysis was initially run under regular interval during electro-pump life. Some series of tests were then conducted under the operating hours of stone crasher. Vibration data was regularly collected. The overall vibration data produced by vibration analysis was compared with previous data, in order to quantify the effectiveness of the results of vibration condition monitoring technique. Numerical data produced by vibration analysis were compared with vibration spectra in standard condition of healthy machine, in order to quantify the effectiveness of the vibration condition monitoring technique. The results of this paper have given more understanding on the dependent roles of vibration analysis in predicting and diagnosing machine faults.
A Doppler Transient Model Based on the Laplace Wavelet and Spectrum Correlation Assessment for Locomotive Bearing Fault Diagnosis  [PDF]
Changqing Shen,Fang Liu,Dong Wang,Ao Zhang,Fanrang Kong,Peter W. Tse
Sensors , 2013, DOI: 10.3390/s131115726
Abstract: The condition of locomotive bearings, which are essential components in trains, is crucial to train safety. The Doppler effect significantly distorts acoustic signals during high movement speeds, substantially increasing the difficulty of monitoring locomotive bearings online. In this study, a new Doppler transient model based on the acoustic theory and the Laplace wavelet is presented for the identification of fault-related impact intervals embedded in acoustic signals. An envelope spectrum correlation assessment is conducted between the transient model and the real fault signal in the frequency domain to optimize the model parameters. The proposed method can identify the parameters used for simulated transients (periods in simulated transients) from acoustic signals. Thus, localized bearing faults can be detected successfully based on identified parameters, particularly period intervals. The performance of the proposed method is tested on a simulated signal suffering from the Doppler effect. Besides, the proposed method is used to analyze real acoustic signals of locomotive bearings with inner race and outer race faults, respectively. The results confirm that the periods between the transients, which represent locomotive bearing fault characteristics, can be detected successfully.
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