An Approach to Acquire Vibration Signals for Gear Fault Detection

2021 ◽  
pp. 153-163
Author(s):  
Vikas Sharma ◽  
Anand Parey
Keyword(s):  
Fault Detection ◽  
Vibration Signals ◽  
Gear Fault

scholarly journals Diagnosis of Localized Faults in Multistage Gearboxes: A Vibrational Approach by Means of Automatic EMD-Based Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-22 ◽  
Author(s):  
M. Buzzoni ◽  
E. Mucchi ◽  
G. D’Elia ◽  
G. Dalpiaz
Keyword(s):  
Fault Detection ◽  
Vibration Analysis ◽  
Vibration Signal ◽  
Vibration Signals ◽  
Condition Indicators ◽  
Intermediate Shaft ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Vibration Signature ◽  
Localized Faults

The gear fault diagnosis on multistage gearboxes by vibration analysis is a challenging task due to the complexity of the vibration signal. The localization of the gear fault occurring in a wheel located in the intermediate shaft can be particularly complex due to the superposition of the vibration signature of the synchronous wheels. Indeed, the gear fault detection is commonly restricted to the identification of the stage containing the faulty gear rather than the faulty gear itself. In this context, the paper advances a methodology which combines the Empirical Mode Decomposition and the Time Synchronous Average in order to separate the vibration signals of the synchronous gears mounted on the same shaft. The physical meaningful modes are selected by means of a criterion based on Pearson’s coefficients and the fault detection is performed by dedicated condition indicators. The proposed method is validated taking into account simulated vibrations signals and real ones.

scholarly journals Gear Fault Detection Using Vibration Analysis and Continuous Wavelet Transform

2014 ◽  
Vol 5 ◽  
pp. 1846-1852 ◽  
Author(s):  
Kiran Vernekar ◽  
Hemantha Kumar ◽  
K.V. Gangadharan
Keyword(s):  
Wavelet Transform ◽  
Fault Detection ◽  
Continuous Wavelet Transform ◽  
Vibration Analysis ◽  
Continuous Wavelet ◽  
Gear Fault

scholarly journals Decomposition of Vibration Signals into Deterministic and Nondeterministic Components and its Capabilities of Fault Detection and Identification

2009 ◽  
Vol 19 (2) ◽  
pp. 327-335 ◽  
Author(s):  
Tomasz Barszcz
Keyword(s):  
Fault Detection ◽  
Adaptive Filter ◽  
Complex Structure ◽  
Reference Signal ◽  
The Self ◽  
Noise Component ◽  
Vibration Signals ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Self Adaptive

Decomposition of Vibration Signals into Deterministic and Nondeterministic Components and its Capabilities of Fault Detection and IdentificationThe paper investigates the possibility of decomposing vibration signals into deterministic and nondeterministic parts, based on the Wold theorem. A short description of the theory of adaptive filters is presented. When an adaptive filter uses the delayed version of the input signal as the reference signal, it is possible to divide the signal into a deterministic (gear and shaft related) part and a nondeterministic (noise and rolling bearings) part. The idea of the self-adaptive filter (in the literature referred to as SANC or ALE) is presented and its most important features are discussed. The flowchart of the Matlab-based SANC algorithm is also presented. In practice, bearing fault signals are in fact nondeterministic components, due to a little jitter in their fundamental period. This phenomenon is illustrated using a simple example. The paper proposes a simulation of a signal containing deterministic and nondeterministic components. The self-adaptive filter is then applied—first to the simulated data. Next, the filter is applied to a real vibration signal from a wind turbine with an outer race fault. The necessity of resampling the real signal is discussed. The signal from an actual source has a more complex structure and contains a significant noise component, which requires additional demodulation of the decomposed signal. For both types of signals the proposed SANC filter shows a very good ability to decompose the signal.

Fault Detection and Discrimination of Rotating Machinery Using Frequency Symptom Parameter and Bayesian Network

2013 ◽  
Vol 470 ◽  
pp. 683-688
Author(s):  
Hai Yang Jiang ◽  
Hua Qing Wang ◽  
Peng Chen
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Bayesian Network ◽  
Frequency Domain ◽  
Evaluation Method ◽  
Roller Bearing ◽  
Rotating Machinery ◽  
Discrimination Index ◽  
Vibration Signals ◽  
Diagnosis Method

This paper proposes a novel fault diagnosis method for rotating machinery based on symptom parameters and Bayesian Network. Non-dimensional symptom parameters in frequency domain calculated from vibration signals are defined for reflecting the features of vibration signals. In addition, sensitive evaluation method for selecting good non-dimensional symptom parameters using the method of discrimination index is also proposed for detecting and distinguishing faults in rotating machinery. Finally, the application example of diagnosis for a roller bearing by Bayesian Network is given. Diagnosis results show the methods proposed in this paper are effective.

Intelligent Gear Fault Identification Method Based on Harmonic Wavelet Package Energy Distribution and Grey Incidence

2013 ◽  
Vol 694-697 ◽  
pp. 1155-1159
Author(s):  
Wen Bin Zhang ◽  
Yan Ping Su ◽  
Yan Jie Zhou ◽  
Ya Song Pu
Keyword(s):  
Energy Distribution ◽  
Fault Identification ◽  
Morphological Filter ◽  
Original Signal ◽  
Energy Distributions ◽  
Vibration Signals ◽  
Fault Pattern ◽  
Gear Fault ◽  
Harmonic Wavelet ◽  
Gear Fault Diagnosis

In this paper, a novel intelligent method to identify gear fault pattern was approached based on morphological filter, harmonic wavelet package and grey incidence. At first, the line structure element was selected for morphological filter to denoise the original signal. Secondly, different gear fault signals were decomposed into eight frequency bands by harmonic wavelet package in three levels; and energy distribution of each band was calculated. Finally, these energy distributions could serve as the feature vectors, the grey incidence of different gear vibration signals was calculated to identify the fault pattern and condition. Practical results show that this method can be used in gear fault diagnosis effectively.

scholarly journals Engine Fault Detection Approach Based on Angle Domain Signal Model

2021 ◽  
Vol 2068 (1) ◽  
pp. 012034
Author(s):  
Hai Zeng ◽  
Ning Zeng ◽  
Jin Han ◽  
Yan Ding
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Failure Detection ◽  
Vibration Signal ◽  
Bench Test ◽  
Signal Model ◽  
Vibration Signals ◽  
Engine Vibration ◽  
Detection Approach ◽  
Diagnosis Approach

Abstract Engine vibration signals include strong noise and non-stationary signals. By the time domain signal processing approach, it is hard to extract the failure features of engine vibration signals, so it is hard to identify engine failures. For improving the success rate of engine failure detection, an engine angle domain vibration signal model is established and an engine fault detection approach based on the signal model is proposed. The angle domain signal model reveals the modulation feature of the engine angular signal. The engine fault diagnosis approach based on the angle domain signal model involves equal angle sampling and envelope analysis of engine vibration signals. The engine bench test verifies the effectiveness of the engine fault diagnosis approach based on the angle domain signal model. In addition, this approach indicates a new path of engine fault diagnosis and detection.

A positive energy residual (PER) based planetary gear fault detection method under variable speed conditions

2019 ◽  
Vol 117 ◽  
pp. 347-360 ◽  
Author(s):  
Jungho Park ◽  
Moussa Hamadache ◽  
Jong M. Ha ◽  
Yunhan Kim ◽  
Kyumin Na ◽  
...  
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Planetary Gear ◽  
Positive Energy ◽  
Variable Speed ◽  
Gear Fault
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