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Broken rotor bar (BRB) is a common fault in induction motors (IM), which results in devastating consequences in the stator and reduces system reliability if not detected in a timely manner. An effective monitoring method of BRB is desired to guarantee production efficiency and service life of IM. Currently, most of the detection methods face low recognition accuracy among different BRB fault types, hence the accurate and reliable diagnosis of BRB faults is still a challenging task. Vibration signal contains substantial information reflecting the real working conditions of IM. Therefore, this paper presents an optimized application of cyclic modulation spectrum (CMS) based on vibration analysis to improve the BRB diagnosis, in which the spectral coherence and enhanced envelop spectrum (EES) is used to extract fault features more clearly. The STFT is a critical step in CMS analysis, therefore it is essential to determine the appropriate window function utilized in STFT according to BRB fault signatures in IM. Finally, the experimental analysis under a wide range of working conditions reveals the CMS with selected Kaiser (β = 3) window function can provide more accurate diagnostic information for different BRB severity compared to the popular envelope analysis.

Accurate state of health (SOH) estimation of lithium-ion batteries is of great importance to ensure the reliability and safety of battery management systems (BMS). The difficulty of modelling the complex degradation mechanism has made the data-driven methods gain much attention in battery SOH prediction. To improve the estimation accuracy of battery SOH, extracting the suitable health indicators is still a challenging work. In this work, the health indication features are attracted from the charging voltage profile based on the experimental data measured under constant current charging mode. Subsequently, the Pearson correlation coefficient is used to evaluate the relationships between the extracted health features and battery capacity, thus selecting the most effective health features for establishing the prediction models. Finally, the battery SOH is estimated using a Gaussian process regression (GPR) method. The estimation results with R 2 of 1 and lower mean absolute error (MAE) and maximum error (MAX) provide higher accuracy based on the extracted health feature.

Tooth wear is one of the most common faults in gear transmission systems. However, due to its limited influences on gear dynamics, the condition monitoring of gear wear experiences more challenges. To investigate the relationship between tooth wear and gear dynamics, this study develops an eight-degree of freedom dynamic model which takes into account the gear mesh impact induced by tooth wear. In this model, the gear mesh impact force induced by tooth wear was calculated as an extra input excitation for the gear dynamic model. Besides, a comparative study was carried out to explore the real reason that causes the poor dynamic behaviour due to the wear from two points of view: 1) the reduction in gear mesh stiffness; 2) the increase in gear mesh impact force. The simulation results show that the wear causes a slight reduction in gear mesh stiffness, thus leading to the decrease in the amplitude of gear mesh frequency. This is not consistent with the phenomena observed in the lab experiment which shows the increase in the gear vibrations. The second simulation of impact force demonstrates that the wear causes an increase in the impact force which further leads to the increase in the amplitude of gear mesh frequency. This study investigates the cause for the deterioration in gear dynamics induced by gear wear, which can benefit the early condition monitoring and fault diagnosis of the gear tooth wear.

Cyclostationary analysis has been strongly recognized as an effective demodulation tool in identifying fault features of rotating machinery based on vibration signature analysis. This study improves two current mature cyclostationary approaches, cyclic modulation spectrum (CMS) and fast spectral correlation (Fast-SC), combined with the novel frequency-domain application of Teager Kaiser energy operator (TKEO). They can enhance fault feature identification with the lower computational burden. Firstly, the raw vibration signal is transformed into the time-frequency domain through the short-time Fourier transform (STFT) to realize the conversion of the multi-carrier signal to a multiple signal-carrier signal. Secondly, the TKEO is utilized to enhance the fault feature by taking full advantage of demodulating the mono-component. Finally, the spectral coherence and enhanced envelope spectrum (EES) are calculated to effectively exhibit fault features. The superiority of the proposed methods is successfully validated by the simulation study and diagnosing the broken rotor bar (BRB) and bearing outer race faults of induction motors (IMs) under various operating conditions.

Broken rotor bar (BRB) faults are one of the most common faults in induction motors (IM). One or more broken bars can reduce the performance and efficiency of the IM and hence waste the electrical power and decrease the reliability of the whole mechanical system. This paper proposes an effective fault diagnosis method using the Teager–Kaiser energy operator (TKEO) for BRB faults detection based on the motor current signal analysis (MCSA). The TKEO is investigated and applied to remove the main supply component of the motor current for accurate fault feature extraction, especially for an IM operating at low load with low slip. Through sensing the estimation of the instantaneous amplitude (IA) and instantaneous frequency (IF) of the motor current signal using TKEO, the fault characteristic frequencies can be enhanced and extracted for the accurate detection of BRB fault severities under different operating conditions. The proposed method has been validated by simulation and experimental studies that tested the IMs with different BRB fault severities to consider the effectiveness of the proposed method. The obtained results are compared with those obtained using the conventional envelope analysis methods and showed that the proposed method provides more accurate fault diagnosis results and can distinguish the BRB fault types and severities effectively, especially for operating conditions with low loads.

Induction motors (IMs) are widely used in many manufacturing processes and industrial applications. The harsh work environment, long-time enduring, and overloads mean that it is subjected to broken rotor bar (BRB) faults. The vibration signal of IMs with BRB faults consists of the reliable modulation information used for fault diagnosis. Cyclostationary analysis has been found to be effective in identifying and extracting fault feature. The estimators of cyclic modulation spectrum (CMS) and fast spectral correlation (FSC) based on the short-time fourier transform (STFT) have higher cyclic frequency resolution, which has proven efficient in demodulating second order cyclostationary (CS2) signals. However, these two estimators have limitations of processing the maximum cyclic frequency &alpha, max that is smaller than Fs/2 (Fs is the sampling frequency) according to Nyquist&rsquo, s Theorem. In addition, they have lower carrier frequency resolution due to the fixed window size used in STFT. In order to resolve the initial shortcomings of the CMS and FSC methods, in this paper, we extended the analysis of CMS algorithm based on the continuous wavelet transform (CWT), which enlarged the maximum cyclic frequency range to Fs/2 and provides higher carrier frequency resolution because the CWT has the advantage of multi-resolution analysis. The reliability and applicability of the proposed method for fault components localization were validated by CS2 simulation signals. Compared to CMS and FSC methods, the proposed approach shows better performance by analyzing vibration signals between healthy motor and faulty motor with one BRB fault under 0%, 20%, 40%, and 80% load conditions.