An energy operator approach to joint application of amplitude and frequency-demodulations for bearing fault detection
نویسنده
چکیده
Bearings are among the most frequently used components. Bearing failure could lead to complete stall of a mechanical system, unpredicted productivity loss for production facilities or catastrophic consequence for mission-critical equipment. As such, bearing fault detection and diagnosis is an imperative part of most of preventive maintenance procedures. This paper presents a parameter independent yet simple to implement fault detection technique. The Teager energy operator is tailored to extract both the amplitude and frequency modulations of the vibration signals measured from mechanical systems. The incorporation of the frequency modulation information into the proposed bearing fault detection method has eliminated the need for interference removal steps. As the amplitude demodulation (AD) is also inherent in the energy operator, the fault frequency can be detected from the spectrum of the energytransformed signal. The effectiveness of the proposed method has been validated using both simulated and experimental data. & 2010 Elsevier Ltd. All rights reserved.
منابع مشابه
Rolling Bearing Fault Analysis by Interpolating Windowed DFT Algorithm
This paper focuses on the problem of accurate Fault Characteristic Frequency (FCF) estimation of rolling bearing. Teager-Kaiser Energy Operator (TKEO) demodulation has been applied widely to rolling bearing fault detection. FCF can be extracted from vibration signals, which is pre-treatment by TEKO demodulation method. However, because of strong noise background of fault vibration signal, it is...
متن کاملA Novel Intelligent Fault Diagnosis Approach for Critical Rotating Machinery in the Time-frequency Domain
The rotating machinery is a common class of machinery in the industry. The root cause of faults in the rotating machinery is often faulty rolling element bearings. This paper presents a novel technique using artificial neural network learning for automated diagnosis of localized faults in rolling element bearings. The inputs of this technique are a number of features (harmmean and median), whic...
متن کاملA new technique for bearing fault detection in the time-frequency domain
This paper presents a new Fast Kurtogram Method in the time-frequency domain using novel types of statistical features instead of the kurtosis. For this study, the problem of four classes for Bearing Fault Detection is investigated using various statistical features. This research is conducted in four stages. At first, the stability of each feature for each fault mode is investigated. Then, res...
متن کاملApplication of Signal Processing Tools for Fault Diagnosis in Induction Motors-A Review-Part II
The use of efficient signal processing tools (SPTs) to extract proper indices for the fault detection in induction motors (IMs) is the essential part of any fault recognition procedure. The 2nd part of this two-part paper is, in turn, divided into two parts. Part two covers the signal processing techniques which can be applied to non-stationary conditions. In this paper, all utilized SPTs for n...
متن کاملFault Detection Method on a Compressor Rotor Using the Phase Variation of the Vibration Signal
The aim of this work is the application of the phase variation in vibration signal for fault detection on rotating machines. The vibration signal from the machine is modulated in amplitude and phase around a carrier frequency. The modulating signal in phase is determined after the Hilbert transform and is used, with the Fast Fourier Transform, to extract the harmonics spectrum in phase. This me...
متن کامل