Electrical Motor Current Signal Analysis using a Dynamic Time Warping Method for Fault Diagnosis

Stator current signal of driving motor can be easily measured. Using it in the gearbox fault diagnosis system is inexpensive and suitable for remote monitoring. According to the application of the Motor Current Signal Analysis in machinery fault detection, we present a new gearbox fault diagnosis system. In modern signal processing technology, Stochastic Resonance theory is widely used to improve SNR (signal to noise ratio). Dynamic time warping algorithm is a simple and efficient way of the pattern identified. Combine the Stochastic Resonance theory and dynamic time warping algorithm as the basic theory of fault diagnosis. To realize the development of fault diagnosis software, we use the mixed-programming of MATLAB algorithms library and VC++.

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Research on a feature extraction method for local faults in planetary gearboxes based on improved dynamic time warping

Insight - Non-Destructive Testing and Condition Monitoring ◽

10.1784/insi.2021.63.8.465 ◽

2021 ◽

Vol 63 (8) ◽

pp. 465-471

Author(s):

Shang Zhiwu ◽

Yu Yan ◽

Geng Rui ◽

Gao Maosheng ◽

Li Wanxiang

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Frequency Domain ◽

Extraction Method ◽

Dynamic Time Warping ◽

Spectral Feature ◽

Time Warping ◽

Feature Extraction Method ◽

Time Frequency ◽

Dynamic Time

Aiming at the local fault diagnosis of planetary gearbox gears, a feature extraction method based on improved dynamic time warping (IDTW) is proposed. As a calibration matching algorithm, the dynamic time warping method can detect the differences between a set of time-domain signals. This paper applies the method to fault diagnosis. The method is simpler and more intuitive than feature extraction methods in the frequency domain and the time-frequency domain, avoiding their limitations and disadvantages. Due to the shortcomings of complex calculation, singularity and poor robustness, the paper proposes an improved method. Finally, the method is verified by envelope spectral feature analysis and the local fault diagnosis of gears is realised.

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Motor current signal analysis using deep neural networks for planetary gear fault diagnosis

Measurement ◽

10.1016/j.measurement.2019.05.074 ◽

2019 ◽

Vol 145 ◽

pp. 45-54 ◽

Cited By ~ 10

Author(s):

Feng Li ◽

Xinyu Pang ◽

Zhaojian Yang

Keyword(s):

Neural Networks ◽

Fault Diagnosis ◽

Signal Analysis ◽

Deep Neural Networks ◽

Planetary Gear ◽

Current Signal ◽

Motor Current ◽

Gear Fault ◽

Gear Fault Diagnosis

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Time-Domain Fault Diagnosis Method of Mechanical and Electrical Equipment Based Improved Dynamic Time Wraping

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.693.1539 ◽

2016 ◽

Vol 693 ◽

pp. 1539-1544 ◽

Cited By ~ 1

Author(s):

Zhi Wu Shang ◽

Zhen Wu Liu ◽

Ya Feng Li ◽

Tai Yong Wang

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Time Domain ◽

Dynamic Time Warping ◽

Statistical Parameter ◽

Electrical Equipment ◽

Time Warping ◽

Trend Prediction ◽

Signal Characteristics ◽

Dynamic Time

Dynamic time warping used in speech recognition widely was migrated to fault feature extraction and diagnosis in time domain. Integration of phase compensation, slope weighted, derivative, sliding window connection, fast dynamic time planning method is applied to dynamic time warping method. And a new method of time-domain signal feature extraction and fault diagnostic based on improved dynamic time warping method of mechanical and electrical equipment was proposed. Identification and localization of fault signal characteristics may be done by improving dynamic time warping method to obtain a residual signal sequences with fault characterized sidebands and selecting the statistical characteristic parameters such as peak, RMS, kurtosis spectrum to complete identification and localization of fault signal characteristics. New time-domain fault trend prediction method of mechanical and electrical equipment was established based on new statistical parameter Thikat. A new idea and target was provided for fault diagnosis of mechanical and electrical equipment.

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