Prediction of spalling on a ball bearing by applying the discrete wavelet transform to vibration signals

The rolling bearings are considered as the heart of rotating machinery and early fault diagnosis is one of the biggest challenges during operation. Due to complicated mechanical assemblies, detection of the advancing fault and faults at the incipient stage is very difficult and tedious. This work presents a fuzzy rule based classification of bearing faults using Fuzzy C-means clustering method using vibration measurements. Experiments were conducted to collect the vibration signals of a normal bearing and bearings with faults in the inner race, outer race and ball fault. Discrete Wavelet Transform (DWT) technique is used to decompose the vibration signals into different frequency bands. In order to detect the early faults in the bearings, various statistical features were extracted from this decomposed signal of each frequency band. Based on the extracted features, Fuzzy C-means clustering method (FCM) is developed to classify the faults using suitable membership functions and fuzzy rule base is developed for each class of the bearing fault using labeled data. The experimental results show that the proposed method is able to classify the condition of the bearing using the extracted features. The proposed FCM based clustering and classification model provides easier interpretation and implementation for monitoring the condition of the rolling bearings at an early stage and it will be helpful to take the preventive action before a large-scale failure.

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Application of discrete wavelet transform for detection of ball bearing race faults

Tribology International ◽

10.1016/s0301-679x(02)00063-4 ◽

2002 ◽

Vol 35 (12) ◽

pp. 793-800 ◽

Cited By ~ 183

Author(s):

S. Prabhakar ◽

A.R. Mohanty ◽

A.S Sekhar

Keyword(s):

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Ball Bearing ◽

Discrete Wavelet ◽

Bearing Race

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Fault Size Estimation in the Outer Race of Ball Bearing Using Discrete Wavelet Transform of the Vibration Signal

Procedia Technology ◽

10.1016/j.protcy.2014.08.003 ◽

2014 ◽

Vol 14 ◽

pp. 12-19 ◽

Cited By ~ 38

Author(s):

S. Khanam ◽

N. Tandon ◽

J.K. Dutt

Keyword(s):

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Ball Bearing ◽

Vibration Signal ◽

Discrete Wavelet ◽

Size Estimation ◽

Outer Race

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Fault detection and diagnosis of a 12-cylinder trainset diesel engine based on vibration signature analysis and neural network

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406218778313 ◽

2018 ◽

Vol 233 (6) ◽

pp. 1910-1923 ◽

Cited By ~ 9

Author(s):

Alireza Zabihi-Hesari ◽

Saeed Ansari-Rad ◽

Farzad A Shirazi ◽

Moosa Ayati

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Diesel Engine ◽

Fault Detection ◽

Discrete Wavelet Transform ◽

Condition Monitoring ◽

Intake Manifold ◽

Discrete Wavelet ◽

Vibration Signals ◽

Time Frequency

This paper presents a condition monitoring and combustion fault detection technique for a 12-cylinder 588 kW trainset diesel engine based on vibration signature analysis using fast Fourier transform, discrete wavelet transform, and artificial neural network. Most of the conventional fault diagnosis techniques in diesel engines are mainly based on analyzing the difference of vibration signals amplitude in the time domain or frequency spectrum. Unfortunately, for complex engines, the time- or frequency-domain approaches do not provide appropriate features solely. In the present study, vibration signals are captured from both intake manifold and cylinder heads of the engine and were analyzed in time-, frequency-, and time–frequency domains. In addition, experimental data of a 12-cylinder 588 kW diesel engine (of a trainset) are captured and the proposed method is verified via these data. Results show that power spectra of vibration signals in the low-frequency range reliably distinguish between normal and faulty conditions. However, they cannot identify the fault location. Hence, a feature extraction method based on discrete wavelet transform and energy spectrum is proposed. The extracted features from discrete wavelet transform are used as inputs in a neural network for classification purposes according to the location of sensors and faults. The experimental results verified that vibration signals acquired from intake manifold have more potential in fault detection. In addition, the capacity of discrete wavelet transform and artificial neural network in detection and diagnosis of faulty cylinders subjected to the abnormal fuel injection was revealed in a complex diesel engine. Beside condition monitoring of the engine, a two-step fault detection method is proposed, which is more reliable than other one-step methods for complex engines. The average condition monitoring performance is from 93.89% up to 99.17%, based on fault location and sensor placement, and the minimum classification performance is 98.34%.

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