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Fault Diagnosis of Intershaft Bearings Using Fusion Information Exergy Distance Method 时间:2018-09-01 09:46 来源:未知作者:admin 点击: 次 Abstract:For the fault diagnosis of intershaft bearings, the fusion information exergy distance method (FIEDM) is proposed by fusing four information exergies, such as singular spectrum exergy, power spectrum exergy, wavelet energy spectrum exergy, and wavelet space spectrum exergy, which are extracted from acoustic emission (AE) signals under multiple rotational speeds and multimeasuring points. The theory of FIEDM is investigated based on four information exergy distances under multirotational speeds. As for rolling bearings, four faults and one normal condition are simulated on a birotor test rig to collect the AE signals, in which the four faults are inner ring fault, outer ring fault, rolling element fault, and inner race-rolling element coupling fault. The faults of the intershaft bearings are analyzed and diagnosed by using the FIEDM. From the investigation, it is demonstrated that the faults of the intershaft bearings are accurately diagnosed and identified, and the FIEDM is effective for the analysis and diagnosis of intershaft bearing faults. Furthermore, the fault diagnosis precision of intershaft bearings becomes higher with increasing rotational speed.
1. Introduction
In the birotor system of an aeroengine, the intershaft bearing is widely used to connect high- and low-pressure rotors in a rotor supporting system. The outer and inner rings of intershaft bearing rotate with the high- and low-pressure rotor, respectively. It is easy for an intershaft bearing to produce a fault owing to the extreme operation environment under a high temperature, high speed, large dynamic load, and poor lubrication. As an essential component of rotors, therefore, the intershaft bearing is an important fault source of rotor system and seriously affects the safety and reliability of aeroengines and even aircrafts [1]. Due to the aeroengine operation in so severe environments, the faults of the intershaft bearings always contain noise, low signal to noise ratio (S/N) signals, and coupling fault signals, so that it is difficult to efficaciously analyze and identify the faults of intershaft bearings [2]. Acoustic emission (AE) signals possess high-frequency fault characteristics and are widely considered as the objective of study in intershaft bearing fault diagnosis. However, the sampling frequency of an AE signal is required to be very high, so that it is problematic to reflect the process characteristics of the intershaft bearing operation by adopting only one transient signal. In addition, the coupling fault of the bearing is difficult to be accurately identified because of the interactions of two or numerous faults.
In the past, various scholars investigated fault diagnosis techniques for rolling bearings based on AE signals using theory and experiments. Al-Ghamdi and Mba [3] validated that AE signal was superior to vibration signal in the analyses and diagnoses of rolling bearings incipient faults. Safizadeh [4] adopted a multisensor data integration method for the vibration fault diagnosis of rolling element bearings utilizing an accelerometer and a load cell. Purushotham et al. [5] proposed the hidden Markov model to conduct the diagnoses of single and coupling faults based on simulation experiments. Al-Bugharbee and Trendafilova [6] researched the fusion approach based on a singular spectrum analysis and an autoregressive (AR) model, to improve the S/N of bearing fault signals, and then to accurately identify the fault categories and defect sizes. Yang et al. [7] extracted the early features of bearing faults and effectively restrained the noise signals for a wind turbine based on the ART-2-information fusion technique. Shakya et al. [8] improved fault diagnosis by obtaining different physical parameters from different sensors. Moosavian et al. [9] utilized the wavelet technique and D-S evidence inference to process the noise and vibration signals in a mechanical fault diagnosis with a diagnostic precision of 98.56%. The above works show that the multiclass information fusion of multiple sensors is promising to improve the fault diagnostic accuracy. However, a bearing fault always couples with noise signals and thus is often the weak and low S/N fault. In respect of complex bearing fault signals, it is difficult for the above methods to process the bearing fault signals because the effective features of weak faults cannot be recognized and extracted.