Pratyay Konar

Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs)

Condition monitoring of induction motors is a fast emerging technology in the field of electrical equipment maintenance and has attracted more and more attention worldwide as the number of unexpected failure of a critical system can be avoided. Keeping this in mind a bearing fault detection scheme of three-phase induction motor has been attempted. In the present study, Support Vector Machine (SVM) is used along with continuous wavelet transform (CWT), an advanced signal-processing tool, to analyze the frame vibrations during start-up. CWT has not been widely applied in the field of condition monitoring although much better results can been obtained compared to the widely used DWT based techniques. The encouraging results obtained from the present analysis is hoped to set up a base for condition monitoring technique of induction motor which will be simple, fast and overcome the limitations of traditional data-based models/techniques.

Multi-class fault diagnosis of induction motor using Hilbert and Wavelet Transform

Two powerful signal processing tools CWT and HT used for feature extraction.A novel approach to find the important fault frequencies using GA.Seven important fault frequencies found to exist in frequency range 50-300Hz. A new method of selecting CWT scales according to the fault frequencies found.Only vertical frame vibration found sufficient for multi-class fault diagnosis.Six induction motor faults considered and successfully detected. The information extraction capability of two widely used signal processing tools, Hilbert Transform (HT) and Wavelet Transform (WT), is investigated to develop a multi-class fault diagnosis scheme for induction motor using radial vibration signals. The vibration signals are associated with unique predominant frequency components and instantaneous amplitudes depending on the motor condition. Using good systematic and analytical approach this fault frequencies can be identified. However, some faults either electrical or mechanical in nature are associated with same or similar vibration frequencies leading to erroneous conclusions. Genetic Algorithm (GA) is proposed and used successfully to find the most relevant fault frequencies in radial (vertical) frame vibration signal which can be used to diagnose the induction motor faults very effectively even in the presence of noise. The information obtained by Continuous Wavelet Transform (CWT) was found to be highly redundant compared to HT and thus by selecting the most relevant features using GA, the fault classification accuracy has considerably improved especially for CWT. Almost similar fault frequencies were found using CWT+GA and HT+GA for radial vibration signal.

Knowledge extraction using data mining for multi-class fault diagnosis of induction motor

The feature extraction capability of rough set theory (RST) and genetic algorithm (GA) are used to extract knowledge from radial frame vibration signal for fault diagnosis of induction motor. This knowledge can assist in selecting scales for continuous wavelet transform (CWT) and mono-components required for Hilbert transform (HT) to extract fault related information from the vibration signal. Thus, the computational complexity of the signal processing tools is considerably reduced making both CWT and HT hardware friendly and suitable for real-time applications. For machine learning based automatic multi-class fault diagnosis, the performance of the classifiers are also considerably improved with significant reduction in computational burden since the redundant and irrelevant information can be effectively removed. The information obtained using data mining technique is successfully used to detect six types of induction motor faults. The results obtained are also verified in presence of high level of noise which has not been attempted earlier. The main contribution of the paper is to combine the advantages of two powerful signal processing tool like CWT and HT to extract hidden information from vibration signal in conjunction with data mining technique making them computationally efficient and easy to implement. A novel but simple approach to remove redundant and irrelevant information, to extract the important knowledge (fault features) using data mining.Knowledge generated using CWT are redundant whereas that from HT are mostly irrelevant. Useful knowledge extracted using RST and GA.Performances of the data mining techniques have been judged by taking into account their applicability in two different types of dataset.Only radial (vertical) frame vibration is found sufficient for multi-class fault diagnosis of induction motor.

Rough set based multi-class fault diagnosis of induction motor using Hilbert Transform

The paper proposes a Rough-set Theory based methodology for multi-class fault diagnosis of induction motors using Hilbert Transform (HT). Depending on the motor condition the vibration signals are associated with unique predominant frequency components and instantaneous amplitudes. The axial vibration signals acquired through data acquisition system are split into different mono-components using Kaiser windowed FIR band pass filter. Statistical features of the Hilbert coefficients obtained from the mono-component signals are used as attributes for fault classification. Rough-set theory is successfully applied for dimensionality reduction of the attributes (by 67%) with almost no degradation of classification accuracy. The proposed Rough-set-Hilbert model eliminates the limitation of wavelet based fault diagnosis technique. The computational efficiency of the proposed classifiers increase due to selection of most relevant features, even at a low sampling frequency of 5120 Hz.

Fault diagnosis of induction motor using CWT and rough-set theory

The paper proposes a Rough-Set CWT based algorithm for multi-class fault diagnosis of induction motor. Use of powerful signal processing technique like CWT drastically reduces the hardware (sensor) requirement of the diagnostic system. Only axial vibration signal is enough to classify seven different types of motor faults. Moreover, successful application of Rough Set theory has enabled to select most relevant CWT scales and corresponding coefficients. Thus, the inherent deficiencies and limitations of CWT are eliminated. Consequently, the computational efficiency has also improved to a great extend. With reduction of attributes by 65% the classification accuracy of the classifiers is very consistent even in presence of high level of noise and with a low frequency sampling frequency of 5120 Hz.

Mechanical fault diagnosis of induction motor using Hilbert pattern

This paper deals with mechanical fault diagnosis in three-phase induction motor from radial vibration measurement. The Hilbert pattern of the 50 Hz mono-component signal extracted from the steady state vibration signature is analyzed and found to contain useful information needed for diagnosing different mechanicals faults. Since Hilbert transform can only be applied to a mono-component signal, Kaiser windowed FIR band pass filter is used to extract the monocomponent signal. Complex analytic signal is generated by using the mono-component signal as the real part and its Hilbert Transform as the imaginary part. The concept of Hilbert transform for extraction of the instantaneous amplitude and frequency is utilized to extract important fault information from the non-stationary vibration signal and found to be quite efficient. This method does not require the analysis of fault frequency components which are slip dependent. Finally, an automatic diagnosis algorithm is attempted using SVM. The proposed method is almost independent of loading condition of the motor and has consistent performance even in presence of high level of noise.

A wavelet-based fault localization in transmission network

The proposed technique consists of a pre-processing unit based on Continuous Wavelet Transform (CWT) in combination with an Artificial Neural Networks (ANN). CWT acts as an extractor of distinctive features in the transient current signals at the sending and receiving end of the transmission line. This information is then fed to the ANN for detecting the fault type and location of fault. The results presented clearly indicate that the present technique is very fast, computationally efficient and intelligent enough to accurately identify three different types of fault (LG & LLG) and their locations.

FPGA friendly fault detection technique for drive fed induction motor

FPGA based embedded system for continuous online monitoring has gained importance in recent years. The existing FPGA based methodologies rely on transient analysis, which unnecessarily gives undue stress to the motor. Also, FFT is used which consumes large resource in the hardware unit. In this paper a DWT based algorithm is designed and implemented in FPGA to detect Broken rotor bar fault using vibration signal at low loading condition at steady state. The main contribution of this work is considerable reduction of hardware resource by use of 2-length filter for DWT and RMS energy based decision block for fault detection. In addition, there is flexibility in extending the algorithm for multi-class fault detection by controlling the required frequency band of DWT.

Application of data mining in fault diagnosis of induction motor

Data driven approaches are gaining popularity in the field of condition monitoring due to their knowledge based fault identification capability for wide range of motor operation. Particularly the method, based on mining the data can encompass the wide behavioral operation of induction motor drive system in industries. Therefore, appropriate low cost instrumentation embedding an efficient algorithm becomes the industrial demand for fault diagnosis of induction motor drive. A hardware friendly algorithm for multi-class fault diagnosis by applying data mining technique is proposed in this paper. Most frequently associated faults like bearing fault, stator inter-turn fault, broken rotor bar fault are investigated for a drive fed induction motor. Discrete wavelet transform-Inverse discrete wavelet transform (DWT-IDWT) algorithm is used to obtain the unique characteristics from each synthesized sub-band and these filtered signals are exploited for feature extraction. A feature selection technique based on Genetic Algorithm (GA) is utilized to identify the potential features for reducing the dimensionality of the feature space. The use of smallest length filter of 2 coefficients (db1) for DWT-IDWT algorithm and 6 relevant features has made the proposed algorithm computationally efficient. The classification accuracy for the investigated multiple faults are found to be quite appreciable. Further, a comparative study is also done using different classifiers: k-NN, MLP and RBF.

Tri-Axial Vibration Analysis Using Data Mining for Multi Class Fault Diagnosis in Induction Motor

Induction motor frame vibration is believed to contain certain crucial information which not only helps detecting faults but also capable of diagnosing different types of faults that occur. The vibration data can be in radial, axial and tangential directions. The frequency content of the three different directions are compared and analyzed using data mining techniques to find the most informative vibration data and to extract the vital information that can be effectively used to diagnose multiple induction motor faults. The vibration data is decomposed using powerful signal processing tools like Continuous Wavelet Transform CWT and Hilbert Transform HT. Statistical features are extracted from the decomposition coefficients obtained. Finally, data mining is applied to extract knowledge. Three types of data mining tools are deployed: sequential greedy search GS, heuristic genetic algorithm GA and deterministic rough set theory RST. The classification accuracy is judged by five types of classifiers: k-Nearest Neighbors k-NN, Multilayer Perceptron MLP, Radial Basis Function RBF and Support Vector Machine SVM, and Simple logistic. The benefits of using all the tri-axial data combined for vibration monitoring and diagnostics is also explored. The results indicate that tri-axial vibration combined provides the most informative knowledge for multi-class fault diagnosis in induction motor. However, it was also found that multi-class fault diagnosis can also be done quite effectively using only the tangential vibration signal with the help of data mining knowledge discovery.