Levent Eren

Bearing damage detection via wavelet packet decomposition of the stator current

Bearing faults are one of the major causes of motor failures. The bearing defects induce vibration, resulting in the modulation of the stator current. In this paper, the stator current is analyzed via wavelet packet decomposition to detect bearing defects. The proposed method enables the analysis of frequency bands that can accommodate the rotational speed dependence of the bearing defect frequencies. The wavelet packet decomposition also provides a better treatment of nonstationary stator current than currently used Fourier techniques.

Detecting motor bearing faults

Three-phase induction motors are the workhorses of industry because of their widespread use. They are used extensively for heating, cooling, refrigeration, pumping, conveyors, and similar applications. They offer users simple, rugged construction, easy maintenance, and cost-effective pricing. These factors have promoted standardization and development of a manufacturing infrastructure that has led to a vast installed base of motors; more than 90% of all motors used in industry worldwide are ac induction motors. Causes of motor failures are bearing faults, insulation faults, and rotor faults. Early detection of bearing faults allows replacement of the bearings, rather than replacement of the motor. The same type of bearing defects that plague such larger machines as 100 hp are mirrored in lower hp machines which has the same type of bearings. Even though the replacement of defective bearings is the cheapest fix among the three causes of failure, it is the most difficult one to detect. Motors that are in continuous use cannot be stopped for analysis. We have developed a circuit monitor for these motors. Incipient bearing failures are detectable by the presence of characteristic machine vibration frequencies associated with the various modes of bearing failure. We will show that circuit monitors that we developed can detect these frequencies using wavelet packet decomposition and a radial basis neural network. This device monitors an induction motors current and defines a bearing failure.

Motor bearing damage detection via wavelet analysis of the starting current transient

Preventive maintenance of induction motors plays an important role in avoiding expensive shut-downs due to motor failures. Motor Current Signature Analysis, MCSA, provides a non-intrusive way to assess the health of a machine. In this paper, the starting current transient of an induction motor is analyzed via discrete wavelet transform to detect bearing faults. The frequency subbands for bearing pre-fault and post-fault conditions are compared to identify the effects of bearing/machine resonant frequencies as the motor starts.

Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks

Early detection of the motor faults is essential and artificial neural networks are widely used for this purpose. The typical systems usually encapsulate two distinct blocks: feature extraction and classification. Such fixed and hand-crafted features may be a suboptimal choice and require a significant computational cost that will prevent their usage for real-time applications. In this paper, we propose a fast and accurate motor condition monitoring and early fault-detection system using 1-D convolutional neural networks that has an inherent adaptive design to fuse the feature extraction and classification phases of the motor fault detection into a single learning body. The proposed approach is directly applicable to the raw data (signal), and, thus, eliminates the need for a separate feature extraction algorithm resulting in more efficient systems in terms of both speed and hardware. Experimental results obtained using real motor data demonstrate the effectiveness of the proposed method for real-time motor condition monitoring.

Harmonic analysis via wavelet packet decomposition using special elliptic half-band filters

The fast Fourier transform (FFT) is the most widely used power system harmonic analysis tool in real-time power metering due to its computational efficiency. Recently, an alternate method, i.e., wavelet packet decomposition (WPD), has been applied to power system signals to meter the voltage and current harmonics. Although the new method provides better analysis, the computational complexity of WPD places a limitation on its use in real-time metering. This paper proposes the use of all-pass-implemented special half-band elliptic infinite-impulse-response filters in the WPD of power system signals. The proposed implementation reduces the computational complexity to levels comparable to FFT.

Adjustable Speed Drive Bearing Fault Detection via Wavelet Packet Decomposition

Adjustable-speed drives perform many vital control functions in the industry, serving in such diverse applications as rolling mills, variable-speed compressors, fans, and pumps. When an adjustable-speed drive fails due to a bearing failure, it is usually catastrophic. Bearing defects introduce vibration anomalies that alter the current characteristic frequencies. This paper addresses the application of motor current signature analysis using wavelet packet decomposition to detect bearing faults in adjustable-speed drives.

Calculation of power system harmonics via wavelet packet decomposition in real time metering

The fast Fourier transform is used widely in harmonic analysis for real time power metering due to its low computational complexity. An alternate method, wavelet packet decomposition, has been applied to power system signals to meter voltage and current harmonics Although the new method provides better analysis, the computational complexity of commonly used filters such as Daubechies, Vaidyanathan, and Beylkin places a limitation on its use in real time metering. This paper proposes an all-pass implementation of Butterworth IIR filters to reduce the computational complexity of the aforementioned FIR filters while providing similar if not better magnitude characteristics.

Neural network based motor bearing fault detection

Bearing faults are the biggest single cause of motor failures. The bearing defects induce vibration resulting in the modulation of the stator current. The stator current can be analyzed via wavelet packet decomposition to detect bearing defects. This method enables the analysis of frequency bands that can accommodate the rotational speed dependence of the bearing defect frequencies. In this study, radial basis function neural networks are used to improve bearing fault detection procedure.

Motor current analysis via wavelet transform with spectral post-processing for bearing fault detection

A Motor current monitoring provides a method for hearing fault detection. At incipient stages of a bearing fault, defect signals are short in duration and small in magnitude. The presence of electrical noise and power harmonics in the current waveform further complicates the hearing fault detection process. The motor current analysis using the continuous wavelet transform with spectral post processing is the method proposed in this paper for incipient bearing fault detection.

Bearing damage detection via wavelet packet

Bearing faults are one of the major causes of motor failures. The bearing defects induce vibration resulting in the modulation of the stator current. In this paper, the stator current is analyzed via wavelet packet decomposition to detect beating defects. The proposed method enables the analysis of frequency bands that can accommodate the rotational speed dependence of the beating-defect frequencies. The wavelet packet decomposition also provides a better treatment of nonstationary stator current than currently used Fourier techniques.