A Novel Method for Bearing Fault Diagnosis under Variable Speed Based on Envelope Spectrum Fault Characteristic Frequency Band Identification

Abstract

Rolling element bearing (REB) vibration signals under variable speed (VS) have non-stationary characteristics. Order tracking (OT) and time-frequency analysis (TFA) are two widely used methods for REB fault diagnosis under VS. However, the effect of OT methods is affected by resampling errors and close-order harmonic interference, while the accuracy of TFA methods is mainly limited by time-frequency resolution and ridge extraction algorithms. To address this issue, a novel method based on envelope spectrum fault characteristic frequency band identification (FCFBI) is proposed. Firstly, the characteristics of the bearing fault vibration signal’s envelope spectrum under VS are analyzed in detail and the fault characteristic frequency band (FCFB) is introduced as a new and effective representation of faults. Then, fault templates based on FCFB are constructed as reference for fault identification. Finally, based on the calculation of the correlation coefficients between the envelope spectrum and fault templates in the extended FCFB, the bearing fault can be diagnosed automatically according to the preset correlation coefficient criterion. Two bearing VS experiments indicate that the proposed method can achieve satisfactory diagnostic accuracy. The comparison of OT and TFA methods further demonstrates the comprehensive superiority of the proposed method in the overall consideration of accuracy, diagnostic time, tachometer dependency, and automatic degree.

Fuzzy Entropy-Assisted Deconvolution Method and Its Application for Bearing Fault Diagnosis

“…where A iv is the amplitude of the ith impulse at VS. This paper assumes that the amplitude of the fault impulses change linearly with the rotational frequency (RF) [35], that is,…”

Advanced Vibration-Based Fault Diagnosis and Vibration Control Methods

“…This Special Issue on “Advanced Vibration-Based Fault Diagnosis and Vibration Control Methods” includes twelve papers ranging across different methods used in the electrical and mechanical industries’ systems. The papers include a good literature review of the current new works on the verification of mechanical properties identification based on the impulse excitation technique and mobile device measurements [ 4 ], the method for bearing fault diagnosis under variable speeds based on envelope spectrum fault characteristic frequency band identification [ 5 ], the interface design of head-worn display applications for condition monitoring in aviation [ 6 ], color recurrence plots for bearing fault diagnoses [ 7 ], the failure mode detection and validation of a shaft-bearing system with common sensors [ 8 ], the strain response and buckling behavior of composite cylindrical shells subjected to external pressure with one end fixed and the other free of boundary conditions [ 9 ], the smartphone-based and data-driven superstructure state prediction method for highway bridges in service [ 10 ], the optimal control algorithm for stochastic systems with parameter drift [ 11 ], the nonlinear vibration control experimental system design of a flexible arm using interactive actuations from shape memory alloy [ 12 ], the design of an active vibration isolation controller with a disturbance observer-based linear quadratic regulator for optical reference cavities [ 13 ], the applicability of touchscreens in manned/unmanned aerial vehicle cooperative missions [ 14 ], and the engineering frequency domain analysis and vibration suppression of flexible aircraft based on an active disturbance rejection controller [ 15 ].…”

“…However, the order tracking method could be influenced by close-order harmonic interference and resampling errors; moreover, the accuracy of the time–frequency analysis method is mainly limited by ridge extraction algorithms and time–frequency resolutions. To address this problem, Pei et al [ 5 ] proposed a method based on envelope spectrum fault characteristic frequency band identification. In this method, the envelope spectrum of the fault vibration signals of the bearings under variable speeds are analyzed, and the fault characteristic frequency band is introduced as a new and effective representation of faults.…”

An ensemble deep learning approach for untrained compound fault diagnosis in bearings under unstable conditions

“…Chen et al [16] proposes a methodology for diagnosing faults in bearings under variable speed conditions by combining techniques such as improved multisynchro-squeezing transform, empirical Fourier decomposition, and generalized demodulation. Pei et al [17] extracted the characteristic frequency band of the fault through envelope spectrum analysis, and calculated the correlation coefficient between the characteristic frequency band of the fault and simulated fault samples. Based on the coefficient value, the type of bearing fault under variable speed conditions was determined.…”

“…Cheng et al [20] proposed a data-driven intelligent fault diagnosis approach for rotating machinery compound fault based on a novel continuous wavelet transformlocal binary CNN. Lin et al [17] developed a compound fault diagnosis system for the gearbox based on CNN. Cheng et al [21] developed a three-stage fault diagnosis method based on variant sparse filtering to identify rotating machinery compound faults.…”