Motor Condition Monitoring Based on Time-Frequency Analysis of Stator Current Signal

Vibration monitoring is an effective method for mechanical fault diagnosis. Wind turbines usually operated under varying-speed condition. Time-frequency analysis (TFA) is a reliable technique to handle such kind of nonstationary signal. In this paper, a new scheme, called current-aided TFA, is proposed to diagnose the planetary gearbox. This new technique acquires necessary information required by TFA from a current signal. The current signal is firstly used to estimate the rotating speed of the shaft. These parameters are applied to the demodulation transform to obtain a rough time-frequency distribution (TFD). Finally, the synchrosqueezing method further enhances the concentration of the obtained TFD. The validation and application of the proposed method are presented by a simulated signal and a vibration signal captured from a test rig.

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Frequency and Time-Frequency Analysis of Cutting Force and Vibration Signals for Tool Condition Monitoring

IEEE Access ◽

10.1109/access.2018.2797003 ◽

2018 ◽

Vol 6 ◽

pp. 6400-6410 ◽

Cited By ~ 29

Author(s):

Juan C. Jauregui ◽

Juvenal R. Resendiz ◽

Suresh Thenozhi ◽

Tibor Szalay ◽

Adam Jacso ◽

...

Keyword(s):

Cutting Force ◽

Condition Monitoring ◽

Frequency Analysis ◽

Tool Condition Monitoring ◽

Vibration Signals ◽

Time Frequency Analysis ◽

Time Frequency ◽

Tool Condition

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application based comparison of statistical versus deep learning approaches to reciprocating compressor valve condition monitoring

Annual Conference of the PHM Society ◽

10.36001/phmconf.2021.v13i1.3081 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Jason Kolodziej ◽

Jacob Chesnes

Keyword(s):

Deep Learning ◽

Condition Monitoring ◽

Frequency Analysis ◽

Reciprocating Compressor ◽

Learning Approaches ◽

Early Assessment ◽

Valve Seat ◽

Repeated Impact ◽

Time Frequency Analysis ◽

Time Frequency

This paper presents a vibration-based condition monitoring approach for early assessment of valve wear in an industrial reciprocating compressor. Valve seat wear is a common fault mode that is caused by repeated impact and accelerated by chatter. Seeded faults consistent with valve seat wear are installed on the head-side discharge valves of a Dresser-Rand ESH-1 industrial reciprocating compressor. Due to the cyclostationary nature of these units a time-frequency analysis is employed where targeted crank angle positions can isolate externally mounted, non-invasive, vibration measurements. A region-of-interest (ROI) is then extracted from the time-frequency analysis and used to train a suitably sized convolutional neural network (CNN). The proposed deep learning method is then compared against a similarly trained discriminant classifier using the same ROIs where features are extracted using texture and shape image statistics. Both methods achieve > 90% success with the CNN classification strategy nearing a perfect result.

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Fault Prediction of Induction Motor Based on Time-Frequency Analysis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.52-54.115 ◽

2011 ◽

Vol 52-54 ◽

pp. 115-120 ◽

Cited By ~ 1

Author(s):

Juggrapong Treetrong

Keyword(s):

Frequency Analysis ◽

Good Accuracy ◽

Fault Prediction ◽

Side Band ◽

Time Frequency Analysis ◽

Time Frequency ◽

Spectrum Method ◽

Rotor Faults ◽

Fault Severity ◽

Motor Condition

Because the faults happening in the motor (such as the stator and the rotor faults) can distort the sinusoidal response of the motor RPM and the main frequency, hence the spectrum method has previously been introduced which it relates to both amplitudes and phases among harmonics in a signal. The method popularly applied for fault detection is based on frequency analysis by observing the side band, its harmonics around main frequencies or its other harmonics. Based on the present experiments, the spectrum method by FFT function has ability to distinguish the motor condition. But, the fault severity levels seem to not able to analyze. Hence the time-frequency Analysis (or spectrogram) of the stator phase currents is proposed here. The method is expected to show relation between the phase current signals and the fault levels which make it can detect the faults and also indicate the fault levels. The experiments show that the proposed method can provide good accuracy for fault prediction and fault level quantification. Hence it can conclude that the propose method can be an effective tool for motor fault prediction.

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