FAULT DETECTION IN A DIESEL ENGINE BY ANALYSING THE INSTANTANEOUS ANGULAR SPEED

2001 ◽  
Vol 15 (3) ◽  
pp. 549-564 ◽  
Author(s):  
JIANGUO YANG ◽  
LIJUN PU ◽  
ZHIHUA WANG ◽  
YICHEN ZHOU ◽  
XINPING YAN
Keyword(s):  
Diesel Engine ◽  
Fault Detection ◽  
Angular Speed ◽  
Instantaneous Angular Speed

scholarly journals Processing of Instantaneous Angular Speed Signal for Detection of a Diesel Engine Failure

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Adam Charchalis ◽  
Mirosław Dereszewski
Keyword(s):  
Diesel Engine ◽  
Measurement Data ◽  
Engine Performance ◽  
Failure Detection ◽  
Angular Speed ◽  
Measured Signal ◽  
Main Task ◽  
Optical Encoder ◽  
Detection Analysis ◽  
Instantaneous Angular Speed

Continuous monitoring of diesel engine performance under its operating is critical for the prediction of malfunction development and subsequently functional failure detection. Analysis of instantaneous angular speed (IAS) of the crankshaft is considered as one of the nonintrusive and effective methods of the detection of combustion quality deterioration. In this paper results of experimental verification of fuel system's malfunction detecting, using optical encoder for IAS recording are presented. The implemented method relies on the comparison of measurement results, recorded under healthy and faulty conditions of the engine. Elaborated dynamic model of angular speed variations enables us to build templates of engine behavior. Recorded during experiment, values of cylinder pressure were taken for the approximation of pressure basic waveform. The main task of data processing is smoothing the raw angular speed signal. The noise is due to sensor mount vibrations, signal emitter machining, engine body vibrations, and crankshaft torsional vibrations. Smoothing of the measurement data was carried out by the implementation of the Savitzky-Golay filter. Measured signal after smoothing was compared with the model of IAS run.

scholarly journals Adaptive Estimation of Instantaneous Angular Speed for Wind Turbine Planetary Gearbox Fault Detection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 49974-49984 ◽  
Author(s):  
Yi Wang ◽  
Baoping Tang ◽  
Lihua Meng ◽  
Bingchang Hou
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Adaptive Estimation ◽  
Angular Speed ◽  
Planetary Gearbox ◽  
Instantaneous Angular Speed

Low speed bearings fault detection and size estimation using instantaneous angular speed

2014 ◽  
Vol 22 (15) ◽  
pp. 3413-3425 ◽  
Author(s):  
W Moustafa ◽  
O Cousinard ◽  
F Bolaers ◽  
K Sghir ◽  
JP Dron
Keyword(s):  
Fault Detection ◽  
Angular Speed ◽  
Size Estimation ◽  
Low Speed ◽  
Instantaneous Angular Speed

A practical signal processing approach for fault detection of axial piston pumps using instantaneous angular speed

2020 ◽  
Vol 234 (19) ◽  
pp. 3935-3947
Author(s):  
Jia-Min Liu ◽  
Li-Chen Gu ◽  
Bao-Long Geng
Keyword(s):  
Fault Detection ◽  
Fault Location ◽  
Signal To Noise Ratio ◽  
Linear Interpolation ◽  
Angular Speed ◽  
Vibration Monitoring ◽  
Transfer Path ◽  
Instantaneous Angular Speed ◽  
Speed Fluctuation ◽  
Axial Piston

The axial piston pump is a core component for power output conversion in the hydraulic system. Monitoring pump status and diagnosing faults in an optimal way are of profound significance for ensuring system reliability. Currently, considerable studies concentrate primarily on the development of vibration-monitoring technologies. However, due to strong fluid shock and noise, vibration analysis has low signal-to-noise ratio and difficulty in fault location. Therefore, an approach of instantaneous angular speed-based fault detection is introduced in this paper since it has the advantages of short transfer path and non-intrusive measurement. Instantaneous angular speed (IAS) is obtained by cross-period linear interpolation (CLI) algorithm for the voltage square wave induced by a magneto-electric tachometer transducer. Compared with the elapsed time method, CLI has the capacity to precisely capture the speed fluctuation. Weighted angle synchronous averaging (WASA) is then utilized to realize the extraction of abnormal wave components in IAS as a vital signal preprocessing method. Instantaneous angular speed fluctuation (IASF) characteristics of the angular domain are analyzed to study the fault diagnosis under varying working conditions. Moreover, it is proven that after processing, the fault features are extracted in the order spectrum where the deterministic shaft order and its harmonics corresponding to wear characteristics are displayed clearly. Experimental results indicate that IAS has demonstrated more effective and sensitive than vibration signals, thus providing a promising tool for the health monitoring of a pump.

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