Rolling element bearing fault detection using acoustic emission signal analyzed by envelope analysis with discrete wavelet transform

2010 ◽  
pp. 596-602 ◽  
Author(s):  
Byeong Su Kim ◽  
Dong Sik Gu ◽  
Jae Gu Kim ◽  
Young Chan Kim ◽  
Byeong Keun Choi
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Fault Detection ◽  
Discrete Wavelet Transform ◽  
Acoustic Emission Signal ◽  
Rolling Element Bearing ◽  
Discrete Wavelet ◽  
Emission Signal ◽  
Rolling Element ◽  
Element Bearing

Assessment of characteristics of acoustic emission parameters for valve damage detection under varying compressor speeds

2020 ◽  
Vol 234 (17) ◽  
pp. 3521-3540
Author(s):  
Hoi Yin Sim ◽  
Rahizar Ramli ◽  
Ahmad Saifizul
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Acoustic Emission Signal ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Crest Factor ◽  
Emission Signal ◽  
Best Parameter ◽  
All Speeds

Acoustic emission technique is often employed to detect valve abnormalities. With the development of technology, machine learning-based fault diagnosis methods are prevalent in the nondestructive testing industry as they can automatically detect valve problems without any human intervention. Nevertheless, feeding in all possible input parameters into the learning algorithm without any prior assessment may result in high computational cost and time, while adding to the risk of having false alarms. This study intended to obtain characteristics of acoustic emission signal for various valve conditions and compressor speeds by examining the four most commonly used parameters, namely the acoustic emission root mean square, acoustic emission crest factor, acoustic emission variance, and acoustic emission kurtosis. The study begins with time–frequency analysis of one revolution acoustic emission signal acquired from a faulty suction valve through discrete wavelet transform to obtain the signal characteristics of valve events. To associate signals with valve movements, the reconstructed discrete wavelet transform signals are further segregated into six time segments, and the four acoustic emission parameters are computed from each of the time segments. These parameters are analyzed through statistical analysis namely the two-way analysis of variance, followed by the Tukey test to obtain the best parameter which can differentiate each valve condition clearly at all speeds. The results revealed that acoustic emission root mean square is the best parameter especially in identification of heavy grease valve condition during suction valve opening event while acoustic emission crest factor is capable to detect leaky valve during the suction valve closing event at all speeds. It is believed that effective valve diagnosis strategy can be delivered by referring to the features of parameters and the characteristic valve event timing corresponding to each valve condition and speed.

Fault Diagnosis Based on Acoustic Emission Signal for Low Speed Rolling Element Bearing

2011 ◽  
Vol 199-200 ◽  
pp. 1020-1023 ◽  
Author(s):  
Hua Qing Wang ◽  
Yong Wei Guo ◽  
Jian Feng Yang ◽  
Liu Yang Song ◽  
Jia Pan ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Acoustic Emission Signal ◽  
Rotating Machinery ◽  
Rolling Element Bearing ◽  
Low Speed ◽  
Rotating Speed ◽  
Emission Signal ◽  
Rolling Element ◽  
Element Bearing

The fault of a bearing may cause the breakdown of a rotating machine, leading to serious consequences. A rolling element bearing is an important part of, and is widely used in rotating machinery. Therefore, fault diagnosis of rolling bearings is important for guaranteeing production efficiency and plant safety. Although many studies have been carried out with the goal of achieving fault diagnosis of a bearing, most of these works were studied for rotating machinery with a high rotating speed rather than with a low rotating speed. Fault diagnosis for bearings under a low rotating speed, is more difficult than under a high rotating speed. Because bearing faults signal is very weak under a low rotating speed. This work acquires vibration and acoustic emission signals from the rolling bearing under low speed respectively, and analyzes the both kinds of signals in time domain and frequency domain for diagnosing the typical bearing faults contrastively. This paper also discussed the advantages using the acoustic emission signal for fault diagnosis of rolling speed bearing. From the results of analysis and experiment we can find the effectiveness of acoustic emission signal is better than vibration signal for fault diagnosis of a bearing under the low speed.

scholarly journals Analytical model and spectral characteristics of acoustic emission signal produced by localized fault of rolling element bearing

2021 ◽  
Vol 39 (4) ◽  
pp. 831-838
Author(s):  
Fazhong Li ◽  
Zengshui He ◽  
Lin Zhang ◽  
Anbo Ming ◽  
Yongsheng Yang
Keyword(s):  
Acoustic Emission ◽  
Analytical Model ◽  
High Frequency ◽  
Spectral Characteristics ◽  
Rolling Element Bearing ◽  
Outer Race ◽  
Emission Signal ◽  
Rolling Element ◽  
Excitation Mechanisms ◽  
Element Bearing

The accurate description of acoustic emission signals produced by the localized fault of a rolling element bearing plays an important role in its feature extraction and analysis. This paper analyzes the excitation mechanisms and develops the analytical model of acoustic emission signals produced when the rolling element bearing passes across the localized fault on the inner or outer race. Based on the analytical model, the spectral characteristics are discussed substantially. Simulations and experiments are carried out to validate the efficacy of the model developed in the paper. The experimental results show that the response signal thus produced has two parts. The first one is produced by the entry of the rolling element bearing, while the other is produced by the departure of the rolling element bearing. The energy of both parts is concentrated around the resonance frequency of the acoustic emission transducer. Generally, the interval of adjacent acoustic emission events is not equivalent to each other and the corresponding spectrum is continuous in the high frequency band.

Rolling Element Bearing Fault Detection Using Redundant Second Generation Wavelet Packet Transform

2011 ◽  
Vol 199-200 ◽  
pp. 931-935 ◽  
Author(s):  
Ning Li ◽  
Rui Zhou
Keyword(s):  
Wavelet Transform ◽  
Fault Detection ◽  
Second Generation ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Bearing Fault ◽  
Rolling Element ◽  
Element Bearing ◽  
Second Generation Wavelet

Wavelet transform has been widely used for the vibration signal based rolling element bearing fault detection. However, the problem of aliasing inhering in discrete wavelet transform restricts its further application in this field. To overcome this deficiency, a novel fault detection method for roll element bearing using redundant second generation wavelet packet transform (RSGWPT) is proposed. Because of the absence of the downsampling and upsampling operations in the redundant wavelet transform, the aliasing in each subband signal is alleviated. Consequently, the signal in each subband can be characterized by the extracted features more effectively. The proposed method is applied to analyze the vibration signal measured from a faulty bearing. Testing results confirm that the proposed method is effective in extracting weak fault feature from a complex background.

Application of Envelop Analysis and Wavelet Transform for Fault Detection in Local Gearboxes Failure

2008 ◽  
Author(s):  
Dong Sik Gu ◽  
Byeong Keun Choi ◽  
Byeong Su Kim ◽  
Jeong Hwan Lee ◽  
Jong Duk Son ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Fault Detection ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Rolling Element ◽  
Gear Fault ◽  
Ae Signal ◽  
Element Bearing ◽  
General Statistical ◽  
New Feature

Vibration analysis is widely used in machinery diagnosis and the wavelet transform has also been implemented in many applications in the condition monitoring of machinery. In contrast to previous applications, this paper examines whether acoustic signal can be used effectively along vibration signal to detect the various local fault, in local fault of gearboxes using the wavelet transform. Moreover, envelop analysis is well known as useful tool for the detection of rolling element bearing fault. In this paper, acoustic emission (AE) sensor is employed to detect gearbox damage by installing them around bearing housing at driven-end side. Signal processing is conducted by wavelet transform and enveloping to detect the fault all at once gearbox and bearing using AE signal. Result of fault detection is presented using some general statistical features and a proposed new feature (RGF: Ratio of Gear Frequency) for gear fault calculated from AE signal with different condition.

Rolling element bearing multi-fault diagnosis using morphological joint time–frequency adaptive kernel–based semi-smart framework

2021 ◽  
pp. 107754632110228
Author(s):  
Sunil Lonare ◽  
Neville Fernandes ◽  
Aditya Abhyankar
Keyword(s):  
Wavelet Transform ◽  
Fault Detection ◽  
Fault Location ◽  
Operating Conditions ◽  
Experimental Results ◽  
Rolling Element Bearing ◽  
Time Frequency ◽  
Rolling Element ◽  
Adaptive Kernel ◽  
Element Bearing

The wavelet transform is a state of the art time–frequency analysis method for rolling element bearing localized fault detection, using vibration signals. When these localized faults are present at more than one location of bearing, it is called “multi-fault.” Using wavelet transform fault detection with high severity is possible, but this method fails to detect the presence of fault as well as the location of a fault in multi-fault case and when the fault severity is low. The identification of the fault location, in rolling element bearing when more than one location of bearing contains a localized fault, is very useful for further root cause analysis; therefore, multi-fault detection is a challenge today. In the present work, a new morphological joint time–frequency adaptive kernel–based semi-smart framework is developed to address this challenge. In morphological joint time–frequency adaptive kernel, the kernel will adapt itself by analyzing the basic morphology of the bearing under observation and by considering the location of a fault. The simulation and experimental results show that morphological joint time–frequency adaptive kernel–based framework is able to detect low severity single fault as well as the location of the localized fault on rolling element bearing in the multi-fault case. Experimental results also show that the morphological joint time–frequency adaptive kernel framework is independent of bearing dimensions as well as machine operating conditions.

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