A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches <s> B. Frequency-Domain Signal-Based Methods <s> This paper is intended as a tutorial overview of induction motors signature analysis as a medium for fault detection. The purpose is to introduce in a concise manner the fundamental theory, main results, and practical applications of motor signature analysis for the detection and the localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. The paper is focused on the so-called motor current signature analysis which utilizes the results of spectral analysis of the stator current. The paper is purposefully written without "state-of-the-art" terminology for the benefit of practising engineers in facilities today who may not be familiar with signal processing. <s> BIB001 </s> A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches <s> B. Frequency-Domain Signal-Based Methods <s> According to gear box combined failure extraction in complex sound field, an acoustic fault detection method based on improved frequency domain blind deconvolution was proposed. Follow the frequency-domain blind deconvolution flow, the morphological filtering was firstly used to extract modulation features embedded in the observed signals, then the CFPA algorithm was employed to do complex-domain blind separation, finally the J-Divergence of spectrum was employed as distance measure to resolve the permutation. Experiments using real machine sound signals was carried out. The result demonstrate this algorithm can be efficiently applied to gear box combined failure detection in practice. <s> BIB002 </s> A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches <s> B. Frequency-Domain Signal-Based Methods <s> Before applying current-signature-analysis-based monitoring methods, it is necessary to thoroughly analyze the existence of the various harmonics on healthy machines. As such an analysis is only done in very few papers, the objective of this paper is to make a clear and rigorous characterization and classification of the harmonics present in a healthy cage rotor induction motor spectrum as a starting point for diagnosis. Magnetomotive force space harmonics, slot permeance harmonics, and saturation of main magnetic flux path through the virtual air-gap permeance variation are taken into analytical consideration. General rules are introduced giving a connection between the number of stator slots, rotor bars, and pole pairs and the existence of rotor slot harmonics as well as saturation-related harmonics in the current spectrum. For certain combinations of stator and rotor slots, saturation-related harmonics are shown to be most prominent in motors with a pole pair number of two or more. A comparison of predicted and measured current harmonics is given for several motors with different numbers of pole pairs, stator slots, and rotor bars. <s> BIB003 </s> A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches <s> B. Frequency-Domain Signal-Based Methods <s> Bearing faults account for a large portion of all faults in wind turbine generators (WTGs). Current-based bearing fault diagnosis techniques have great economic benefits and are potential to be adopted by the wind energy industry. This paper models the modulation effects of bearing faults on the stator currents of a direct-drive wind turbine equipped with a permanent-magnet synchronous generator (PMSG) operating with a variable shaft rotating frequency. Based on the analysis, a method consisting of appropriate current frequency and amplitude demodulation algorithms and a 1P-invariant power spectrum density algorithm is proposed for bearing fault diagnosis of variable-speed direct-drive wind turbines using only one-phase stator current measurements, where 1P frequency stands for the shaft rotating frequency of a wind turbine. Experimental results on a direct-drive wind turbine equipped with a PMSG operating in a wind tunnel are provided to verify the proposed fault diagnosis method. The proposed method is demonstrated to have advantages over the method of directly using stator current measurements for WTG bearing fault diagnosis. <s> BIB004 </s> A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches <s> B. Frequency-Domain Signal-Based Methods <s> Abstract Torsional vibration signals are theoretically free from the amplitude modulation effect caused by time variant vibration transfer paths due to the rotation of planet carrier and sun gear, and therefore their spectral structure are simpler than transverse vibration signals. Thus, it is potentially easy and effective to diagnose planetary gearbox faults via torsional vibration signal analysis. We give explicit equations to model torsional vibration signals, considering both distributed gear faults (like manufacturing or assembly errors) and local gear faults (like pitting, crack or breakage of one tooth), and derive the characteristics of both the traditional Fourier spectrum and the proposed demodulated spectra of amplitude envelope and instantaneous frequency. These derivations are not only effective to diagnose single gear fault of planetary gearboxes, but can also be generalized to detect and locate multiple gear faults. We validate experimentally the signal models, as well as the Fourier spectral analysis and demodulation analysis methods. <s> BIB005
Frequency-domain signal-based method is to detect changes or faults by using spectrum analysis tool such as discrete Fourier transformation. One of the most powerful frequencydomain methods for diagnosing motor faults is motor current signature analysis (MCSA), which utilizes the spectral analysis of the stator current to sense rotor faults associated with broken rotor bars and mechanical balance. Without requiring access to the motor, the MCSA approach has received much attention, which was well reviewed in BIB001 and  . Recent development of current-based spectrum signature analysis for fault diagnosis can be found in BIB003 and BIB004 . Vibration signal analysis is a common method for condition monitoring and diagnosis for mechanical equipment such as gear box, as machine sound indicates a lot about the working condition of the machine. In BIB002 , an acoustic fault detection method was addressed for gear box on the basis of the improved frequency-domain blind deconvolution flow. Recently, in BIB005 , Fourier spectrum and the demodulated spectra of amplitude envelope have been employed to detect and locate multiple gear faults in planetary gearboxes.