Motor current signal analysis using a modified bispectrum for machine fault diagnosis

This paper presents the use of the induction motor current to identify and quantify common faults within a two-stage reciprocating compressor. The theoretical basis is studied to understand current signal characteristics when the motor undertakes a varying load under faulty conditions. Although conventional bispectrum representation of current signal allows the inclusion of phase information and the elimination of Gaussian noise, it produces unstable results due to random phase variation of the sideband components in the current signal. A modified bispectrum based on the amplitude modulation feature of the current signal is thus proposed to combine both lower sidebands and higher sidebands simultaneously and hence describe the current signal more accurately. Based on this new bispectrum a more effective diagnostic feature namely normalised bispectral peak is developed for fault classification. In association with the kurtosis of the raw current signal, the bispectrum feature gives rise to reliable fault classification results. In particular, the low feature values can differentiate the belt looseness from other fault cases and discharge valve leakage and intercooler leakage can be separated easily using two linear classifiers. This work provides a novel approach to the analysis of stator current for the diagnosis of motor drive faults from downstream driving equipment

Automatic Classification of Winding Asymmetries in Wound Rotor Induction Motors based on Bicoherence and Fuzzy C-Means Algorithms of Stray Flux Signals

(c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Wound rotor induction motors are used in a certain number of industrial applications due to their interesting advantages, such as the possibility of inserting external rheostats in series with the rotor winding to enhance the torque characteristics under starting and to decrease the high inrush currents. However, the more complex structure of the rotor winding, compared to cage induction motors, is a source for potential maintenance problems. In this regard, several anomalies can lead to the occurrence of asymmetries in the rotor winding that may yield terrible repercussions for the machine¿s integrity. Therefore, monitoring the levels of asymmetry in the rotor winding is of paramount importance to ensure the correct operation of the motor. This work proposes the use of Bicoherence of the stray flux signal, as an indicator to obtain an automatic classification of the rotor winding condition. For this, the Fuzzy C-Means machine learning algorithm is used, which starts with the Bicoherence calculation and generates the different clusters for grouping and classification, according to the level of winding asymmetry. In addition, an analysis regarding the influence of the flux sensor position on the automatic classification and the failure detection is carried out. The results are highly satisfactory and prove the potential of the method for its future incorporation in autonomous condition monitoring systems that can be satisfactorily applied to determine the health of these machines.This work was supported in part by Generalitat Valenciana, Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital, (project AICO/019/224) and in part by MEC under Project MTM2016-75963-P.Iglesias Martínez, ME.; Antonino-Daviu, JA.; Fernández De Córdoba, P.; Conejero, JA.; Dunai, L. (2021). Automatic Classification of Winding Asymmetries in Wound Rotor Induction Motors based on Bicoherence and Fuzzy C-Means Algorithms of Stray Flux Signals. IEEE Transactions on Industry Applications. 57(6):5876-5886. https://doi.org/10.1109/TIA.2021.3108413S5876588657

A new method of vibration analysis for the diagnosis of impeller in a centrifugal pump

Centrifugal pumps are widely used in many important industries such as power generation plants, chemical processes and petroleum refiners. The condition monitoring of centrifugal pumps is highly regarded by many researchers and users to minimize unexpected break-downs. Impellers are the core parts of pumps but often appear early damages due to flow cav-itation and erosion. This paper investigates a new approach to monitoring the conditions of impellers using surface vibration with advanced signal analysis. As overall vibration respons-es contain high level of broadband noises due to cavities and turbulences, noise reduction is critical to develop reliable and effective features. However, considering the modulation effect between rotating shaft and blade passing components, a modulation signal bispectrum (MSB) method is employed to extract these deterministic characteristics of modulations, which is different from previous researches in that broadband random sources are often used. Experi-mental results show that the diagnostic features developed by MSB allow impellers with inlet vane damages and exit vane faults to be identified under different operating conditions

A multitask-aided transfer learning-based diagnostic framework for bearings under inconsistent working conditions.

Rolling element bearings are a vital part of rotating machines and their sudden failure can result in huge economic losses as well as physical causalities. Popular bearing fault diagnosis techniques include statistical feature analysis of time, frequency, or time-frequency domain data. These engineered features are susceptible to variations under inconsistent machine operation due to the non-stationary, non-linear, and complex nature of the recorded vibration signals. To address these issues, numerous deep learning-based frameworks have been proposed in the literature. However, the logical reasoning behind crack severities and the longer training times needed to identify multiple health characteristics at the same time still pose challenges. Therefore, in this work, a diagnosis framework is proposed that uses higher-order spectral analysis and multitask learning (MTL), while also incorporating transfer learning (TL). The idea is to first preprocess the vibration signals recorded from a bearing to look for distinct patterns for a given fault type under inconsistent working conditions, e.g., variable motor speeds and loads, multiple crack severities, compound faults, and ample noise. Later, these bispectra are provided as an input to the proposed MTL-based convolutional neural network (CNN) to identify the speed and the health conditions, simultaneously. Finally, the TL-based approach is adopted to identify bearing faults in the presence of multiple crack severities. The proposed diagnostic framework is evaluated on several datasets and the experimental results are compared with several state-of-the-art diagnostic techniques to validate the superiority of the proposed model under inconsistent working conditions

The Optimization of Vibration Data Analysis for the Detection and Diagnosis of Incipient Faults in Roller Bearings

The rolling element bearing is a key component of many machines. Accurate and timely diagnosis of its faults is critical for proactive predictive maintenance. The research described in this thesis focuses on the development of techniques for detecting and diagnosing incipient bearing faults. These techniques are based on improved dynamic models and enhanced signal processing algorithms. Various common fault detection techniques for rolling element bearings are reviewed in this work and a detailed experimental investigation is described for several selected methods. Envelope analysis is widely used to obtain the bearing defect harmonics from the spectrum of the envelope of a vibration signal. This enables the detection and diagnosis of faults, and has shown good results in identifying incipient faults occurring on the different parts of a bearing. However, a critical step in implementing envelope analysis is to determine the frequency band that contains the signal component corresponding to the bearing fault (the one with highest signal to noise ratio). The choice of filter band is conventionally made via manual inspection of the spectrum to identify the resonant frequency where the largest change has occurred. In this work, a spectral kurtosis (SK) method is enhanced to enable determination of the optimum envelope analysis parameters, including the filter band and centre frequency, through a short time Fourier transform (STFT). The results show that the maximum amplitude of the kurtogram indicates the optimal parameters of band pass filter that allows both outer race and inner race faults to be determined from the optimised envelope spectrum. A performance evaluation is carried out on the kurtogram and the fast kurtogram, based on a simulated impact signal masked by different noise levels. This shows that as the signal to noise ratio (SNR) reaches as low as -5dB the STFT-based kurtogram is more effective at identifying periodic components due to bearing faults, and is less influenced by irregular noise pulses than the wavelet based fast kurtogram. A study of the accuracy of rolling-bearing diagnostic features in detecting bearing wear processes and monitoring fault sizes is presented for a range of radial clearances. Subsequently, a nonlinear dynamic model of a deep groove ball bearing with five degrees of freedom is developed. The model incorporates local defects and clearance increments in order to gain the insight into the bearing dynamics. Simulation results indicate that the vibrations at fault characteristic frequencies exhibit significant variability for increasing clearances. An increased vibration level is detected at the bearing characteristic frequency for an outer race fault, whereas a decreased vibration level is found for an inner race fault. Outcomes of laboratory experiments on several bearing clearance grades, with different local defects, are used herein for model validation purposes. The experimental validation indicates that the envelope spectrum is not accurate enough to quantify the rolling element bearing fault severity adequately. To improve the results, a new method has been developed by combining a conventional bispectrum (CB) and modulation signal bispectrum (MSB) with envelope analysis. This suppresses the inevitable noise in the envelope signal, and hence provides more accurate diagnostic features. Both the simulation and the experimental results show that MSB extracts small changes from a faulty bearing more reliably, enabling more accurate and reliable fault severity diagnosis. Moreover, the vibration amplitudes at the fault characteristic frequencies exhibit significant changes with increasing clearance. However, the vibration amplitude tends to increase with the severity of an outer race fault and decrease with the severity of an inner race fault. It is therefore necessary to take these effects into account when diagnosing the size of a defect

Motor Fault Diagnosis Using Higher Order Statistical Analysis of Motor Power Supply Parameters

Motor current signature analysis (MCSA) has been an effective method to monitor electrical machines for many years, predominantly because of its low instrumentation cost, remote implementation and comprehensive information contents. However, it has shortages of low accuracy and efficiency in resolving weak signals from incipient faults, such as detecting early stages of induction motor fault. In this thesis MCSA has been improved to accurately detect electrical and mechanical faults in the induction motor namely broken rotor bars, stator faults and motor bearing faults. Motor current signals corresponding to a healthy (baseline) and faulty condition on induction motor at different loads (zero, 25%, 50% and 75% of full load) were rearranged and the baseline current data were examined using conventional methods in frequency domain and referenced for comparison with new modulation signal bispectrum. Based on the fundamental modulation effect of the weak fault signatures, a new method based on modulation signal bispectrum (MSB) analysis is introduced to characterise the modulation and hence for accurate quantification of the signatures. This method is named as (MSB-SE). For broken rotor bar(BRB), the results show that MSB-SE suggested in this research outperforms conventional bispectrum CB significantly for all cases due its high performance of nonlinear modulation detection and random noise suppression, which demonstrates that MSB-SE is an outstanding technique whereas (CB) is inefficient for motor current signal analysis [1] . Moreover the new estimators produce more accurate results at zero, 25%, 50%, 75% of full load and under broken rotor bar, compared with power spectrum analysis. Especially it can easily separate the half BRB at a load as low as 25% from baseline where PS would not produce a correct separation. In case of stator faults, a MSB-SE is investigated to detect different severities of stator faults for both open and short circuit. It shows that MSB-SE has the capability to accurately estimate modulation degrees and suppress the random and non-modulation components. Test results show that MSB-SE has a better performance in differentiating spectrum amplitudes due to stator faults and hence produces better diagnosis performance, compared with that of power spectrum (PS). For motor bearing faults, tests were performed with three bearing conditions: baseline, outer race fault and inner race fault. Because the signals associated with faults produce small modulations to supply component and high noise levels, MSB-SE is used to detect and diagnose different motor bearing defects. The results show that bearing faults can induce detectable amplitude increases at its characteristic frequencies. MSB-SE peaks show a clear difference at these frequencies whereas the conventional power spectrum provides change evidences only at some of the frequencies. This shows that MSB has a better and reliable performance in detecting small changes from the faulty bearing for fault detection and diagnosis. In addition, the study also shows that current signals from motors with variable frequency drive controller have too much noise and it is unlikely to discriminate the small bearing fault component. This research also applies a mathematical model for the simulation of current signals under healthy and broken bars condition in order to further understand the characteristics of fault signature to ensure the methodologies used and accuracy achieved in the detection and diagnosis results. The results show that the frequency spectrum of current signal outputs from the model take the expected form with peaks at the sideband frequency and associated harmonics

Defining the Wavelet Bispectrum

Bispectral analysis is an eective signal processing tool for analysing interactions between oscillations, and has been adapted to the continuous wavelet transform for time-evolving analysis of open systems. However, one unaddressed question for the wavelet bispectrum is quantication of the bispectral content of an area of scale-scale space. This makes the capacity for quantitative rather than merely qualitative interpretation of wavelet bispectrum computations very limited. In this paper, we overcome this limitation by providing suitable normalisations of the wavelet bispectrum formula that enable it to be treated as a density to be integrated. These are roughly analogous to the normalisation for second-order wavelet spectral densities. We prove that our denition of the wavelet bispectrum matches the traditional bispectrum of sums of sinusoids, in the limit as the frequency resolution tends to innity. We illustrate the improved quantitative power of our denition with numerical and experimental data

Compound Fault Diagnosis of Centrifugal Pumps Using Vibration Analysis Techniques

Centrifugal pumps are widely used in many different industrial processes, such as power generation stations, chemical processing plants, and petroleum industries. The problem of failures in centrifugal pumps is a large concern due to its significant influence on such critical industries. Particularly, as the core, parts of a pump, bearings and the impellers are subject to different corrosions and their faults can cause major degradation of pump performances and lead to the breakdown of production. Therefore, an early detection of these types of faults would provide information to take timely preventive actions. This research investigates more effective techniques for diagnosing common faults of impellers and bearings with advanced signal analysis of surface vibration. As overall vibration responses contain a high level of broadband noises due to fluid cavities and turbulences, noise reduction is critical to developing reliable and accurate features. However, considering the modulation effect between the rotating shaft, vane passing components and any structural resonances, a modulation signal bispectrum (MSB) method is mainly used to extract these deterministic characteristics of modulations, which differs from previous researches in that the broadband vibration is often characterised with statistical methods, high frequency demodulation along spectrum analysis. Both theoretical analysis and experimental evaluation show that the diagnostic features developed by MSB allow impellers with inlet vane damages and exit vane faults to be identified under different operating conditions. It starts with an in-depth examination of the vibration excitation mechanisms associated with each type of common pump faults including impeller leakages, impeller blockages, bearing inner race defects and bearing outrace defects. Subsequently, fault diagnosis was carried out using popular spectrum and envelope analysis, and more advanced kurtogram and MSB analysis. These methods all can successfully provide correct detection and diagnosis of the faults, which are induced manually to the test pump. Envelope analysis in a bands optimised with Kurtogram produces outstanding detection results for bearing faults but not the impeller faults in a frequency range as high as several thousand hertz (about 7.5kHz). In addition, it cannot provide satisfactory diagnostic results in separating the faults across different flow rates, especially when the compound faults were evaluated. This deficiency is because they do not have the capability of suppressing the random noises. Meanwhile, it has found that the MSB analysis allows both impeller and bearing faults to be detected and diagnosed. Especially, when the pump operated with compound faults both the fault types and severity can be attained by the analysis with acceptable accuracy for different flow rates. This high performance of diagnosis is due to that MSB has the unique capability of noise reduction and nonlinearity demodulation. Moreover, MSB diagnosis can be a frequency range lower than 2 times of the blade pass frequency (<1kHz), meaning that it can be more cost-effective as it demands lower performance measurement systems. In addition, the study also found that one accelerometer mounted on the pump housing is sufficient to monitor the faults on both the impeller and the bearing as it uses a lower frequency vibration which propagates far away from the bearing to the housing, rather than another accelerometer on the bearing pedestal directly