Seismological system fault detection technology based on waveform analysis

2021 ◽  
Author(s):  
Rongdi An ◽  
Tao Jing ◽  
Yi Sun
Keyword(s):  
Fault Detection ◽  
Waveform Analysis ◽  
Detection Technology

Online Condition Monitoring of Traction Motor

2016 ◽  
pp. 489-523
Author(s):  
Anik Kumar Samanta ◽  
Arunava Naha ◽  
Devasish Basu ◽  
Aurobinda Routray ◽  
Alok Kanti Deb
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Traction Motor ◽  
Motor Current ◽  
Detection Technology ◽  
Current Voltage ◽  
Motor Current Signature Analysis ◽  
Complete Failure ◽  
Condition Monitoring System ◽  
The Cost

Squirrel Cage Induction Motors (SCIMs) are major workhorse of Indian Railways. Continuous online condition monitoring of the SCIMs like Traction Motor (TM) are essential to prevent unnecessary stoppage time in case of a complete failure. Before a complete failure, the TMs generally develop incipient or weak faults. Weak faults have minute influence on the motor performance but eventually leads to complete failure of the motor. If these weak faults are identified at the earliest then, a scheduled maintenance can be planned which will prevent any unplanned stoppage. The signals used for SCIM fault detection are motor current, voltage, vibration, temperature, voltage induced in search coil, etc. The most popular fault detection technology is based on Motor Current Signature Analysis (MCSA). MCSA based online and onboard TM condition monitoring system can be very useful for Indian railways to reduce the cost of operation and unplanned delay by shifting from unnecessary scheduled maintenance to condition-based maintenance of TM and other auxiliary SCIMs.

scholarly journals Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

2020 ◽  
Vol 10 (7) ◽  
pp. 2443
Author(s):  
Huaitao Shi ◽  
Jin Guo ◽  
Xiaotian Bai ◽  
Lei Guo ◽  
Zhenpeng Liu ◽  
...  
Keyword(s):  
Fault Detection ◽  
Nonlinear Dynamic ◽  
Detection Method ◽  
Normal Operation ◽  
Rolling Bearings ◽  
Detection Delay ◽  
Detection Technology ◽  
Non Linear ◽  
Incipient Fault ◽  
Incipient Fault Detection

The incipient fault detection technology of rolling bearings is the key to ensure its normal operation and is of great significance for most industrial processes. However, the vibration signals of rolling bearings are a set of time series with non-linear and timing correlation, and weak incipient fault characteristics of rolling bearings bring about obstructions for the fault detection. This paper proposes a nonlinear dynamic incipient fault detection method for rolling bearings to solve these problems. The kernel function and the moving window algorithm are used to establish a non-linear dynamic model, and the real-time characteristics of the system are obtained. At the same time, the deep decomposition method is used to extract weak fault characteristics under the strong noise, and the incipient failures of rolling bearings are detected. Finally, the validity and feasibility of the scheme are verified by two simulation experiments. Experimental results show that the fault detection rate based on the proposed method is higher than 85% for incipient fault of rolling bearings, and the detection delay is almost zero. Compared with the detection performance of traditional methods, the proposed nonlinear dynamic incipient fault detection method is of better accuracy and applicability.

The Research on the Low Clutch’s Noise Detection Technology which Based on the Wavelet Transform

2012 ◽  
Vol 433-440 ◽  
pp. 4082-4086
Author(s):  
Yue Dong Chen ◽  
Chang Zhong Yu
Keyword(s):  
Wavelet Transform ◽  
Fault Detection ◽  
Detection System ◽  
Rotating Machinery ◽  
Noise Detection ◽  
Machine Vibration ◽  
Bearing Defects ◽  
Detection Technology ◽  
On Line ◽  
Reasonable Choice

The essay introduce the hardware Design based on the Line detection system, and apply the wavelet analysis theory to the low clutch’s fault signal processing to fulfill the low clutch’s noise detection which based on the wavelet transform. Practice shows that the continuous wavelet signal has a strong ability of fault detection, if reasonable choice of wavelet function and various parameters among the fault detection, the local feature of the fault signal can be intuitively got, thus supply the products with a effective tool. The current washing machine clutch all have a washing deceleration function, so it is called as low clutch. As one of the most common parts of rotating machinery, low clutch is also one of the easily damaged parts among the rotating machinery. According to statistics, thirty percent of the rotating machinery’s operational problems caused by the bearing faults[1]. Bearing defects can cause severely machine vibration and generation noise, or even cause damage to the equipment[4]. This article is mainly detect the low clutch’s vibration noise in operation by accelerometer, and deal with the collected data through wavelet transform, thus realize the On-line condition monitoring to the low clutch.

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