Wilson Wang

Self-organizing fuzzy belief inference systems (SOFBISs) have been used in evolving prototype-based classifications. However, the existing SOFBIS models treat all features and statistical descriptors equally, which may limit their performance in applications where input features have unequal diagnostic relevance. This paper presents an evolving fuzzy belief (EFB) technique to enhance pattern classification and machine condition monitoring. Application of a new feature, Manhattan distance measure, is proposed to highlight informative features and suppress less relevant patterns. The feature weights are estimated using class-wise Pearson correlations to emphasize informative features, thereby improving the interpretability and accuracy of fault diagnosis. The effectiveness of the proposed EFB technique for bearing health condition monitoring and fault diagnosis is examined by both simulation and experimental tests.

Although swarm decomposition (SD) can provide flexibility in extracting oscillatory components, it has limitations in parameter sensitivity and the absence of a systematic mode selection, which may obscure weak fault features and lead to incomplete decomposition and high computational costs. To address these limitations, a new recursive swarm decomposition (RSD) technique is presented in this work for more efficient bearing fault detection and fault type diagnosis. The proposed RSD technique employs a two-layer decomposition framework with an impulse-modulation energy (IE) score mode evaluation for component selection. The selected sub-IMFs are then applied in harmonic suppression to attenuate rotational interference and enhance impulsive features. Its effectiveness is verified by simulation and experimental tests. Test results show that the proposed RSD technique can enhance representative features for bearing fault detection.

Rolling element bearings are commonly used in rotating machines. However, reliable and real-time bearing condition monitoring and fault diagnosis is a challenging problem in industry, due to limitations associated with reliable real-time data acquisition (DAQ) and signal processing in noisy environments where bearings are often housed. In this article, a smart sensor-based bearing fault detection technology is proposed. The proposed method introduces a DAQ design for high resolution vibration signal measurement. A wavelet swarm decomposition (WSD) technique is also developed for stand-alone bearing fault detection. The proposed WSD technique adopts a new wavelet filter to adaptively determine center frequencies in the time-frequency domain. Moreover, an ensemble function is introduced to select decomposition modes and improve processing efficiency. The effectiveness of the proposed smart sensor bearing fault detection method is validated experimentally to detect different types of bearing faults. Test results show that the proposed method powered by the developed WSD technique performs better (6% higher) than other related techniques in bearing fault detection and diagnosis.

Rolling element bearings are commonly used in rotating machines. Bearing fault detection and diagnosis play a critical role in machine operations to recognize bearing faults at their early stage and prevent machine performance degradation, improve operation quality, and reduce maintenance costs. Although many fault detection techniques are proposed in the literature for bearing condition monitoring, reliable bearing fault detection remains a challenging task in this research and development field. This study proposes an enhanced Teager–Kaiser (eTK) technique for bearing fault detection and diagnosis. Vibration signals are used for analysis. The eTK technique is novel in two aspects: Firstly, an empirical mode decomposition analysis is suggested to recognize representative intrinsic mode functions (IMFs) with different frequency components. Secondly, an eTK denoising filter is proposed to improve the signal-to-noise ratio of the selected IMF features. The analytical signal spectrum analysis is conducted to identify representative features for bearing fault detection. The effectiveness of the proposed eTK technique is verified by experimental tests corresponding to different bearing conditions.

Accurate prediction of system degradation and remaining useful life (RUL) is essential for reliable health monitoring of Lithium-ion (Li-ion) batteries, as well as other dynamic systems. While evolving systems can offer adequate adaptability to the nonstationary and nonlinear behavior of battery degradation, existing methods often face challenges such as uncontrolled rule growth, limited adaptability, and reduced accuracy under noisy conditions. To address these limitations, this paper presents a smart evolving fuzzy predictor with customized firefly optimization (SEFP-FO) to provide a better solution for battery RUL prediction. The proposed SEFP-FO technique introduces two main contributions: (1) An activation- and distance-aware penalization strategy is proposed to govern rule evolution by evaluating the structural relevance of incoming data. This mechanism can control rule growth while maintaining model convergence. (2) A customized firefly algorithm is suggested to optimize the antecedent parameters of newly generated fuzzy rules, thereby enhancing prediction accuracy and improving the predictor’s adaptive capability to time-varying system conditions. The effectiveness of the proposed SEFP-FO technique is first validated by simulation using nonlinear benchmark datasets, which is then applied to Li-ion battery RUL predictions.

This paper proposes a Hybrid Recursive Trigonometric (HRT) technique for FPGA-based direct digital frequency synthesizers. The HRT technique integrates a recursive cosine generator with periodic reinitialization via a second-order Taylor polynomial to reduce cumulative errors without requiring ROMs or iterative CORDIC units. A resource-efficient combinational architecture is implemented and validated on the Lattice iCE40HX1K FPGA. The effectiveness of the proposed HRT technique is evaluated through simulation and FPGA-based experiments, with respect to spectral accuracy and resource efficiency, particularly for fixed-point cosine waveform synthesis in low-resource digital systems. Simulation results show that the system has a spurious-free dynamic range (SFDR) of −86.09 dBc and signal-to-noise ratio of 52.74 dB using 16-bit fixed-point arithmetic. Experimental measurements confirm the feasibility, achieving −58.86 dBc SFDR.

Induction motors or machines (IMs) are the driving force in various industries such as manufacturing, transportation, and wind power generation. Hence it is essential to detect faults in IMs reliably so as to enhance the production quality and avoid operational degradation. However, it is still challenging to detect faults in IMs reliably as fault feature properties could change under variable IM operating conditions. The objective of this paper is to propose an enhanced empirical mode decomposition (EEMD) technique to detect the IM broken rotor bar fault based on motor current signature analysis. In the proposed EEMD technique, firstly a phase insensitive similarity function is suggested to determine the representative intrinsic mode function (IMF). Secondly, an optimized adaptive multiband filter is proposed to recognize the fault characteristic features from the spectrum. Thirdly, a modified whale optimization (MWO) algorithm is suggested to optimize the parameters in the adaptive multiband filter. A reference function is also proposed to enhance feature properties and IM fault detection. The effectiveness of the proposed EEMD technique is verified experimentally under different IM conditions.

Lithium-ion (Li-ion) batteries have become essential in modern industries and domestic applications due to their high energy density and efficiency. However, they experience gradual degradation over time, which presents significant challenges in maintaining optimal battery performance and increases the risk of unexpected system failures. To ensure the reliability and longevity of Li-ion batteries in applications, various methods have been proposed for battery health monitoring and remaining useful life (RUL) prediction. This paper provides a comprehensive review and analysis of the primary approaches employed for battery health monitoring and RUL estimation under the categories of model-based, data-driven, and hybrid methods. Generally speaking, model-based methods use physical or electrochemical models to simulate battery behaviour, which offers valuable insights into the principles that govern battery degradation. Data-driven techniques leverage historical data, AI, and machine learning algorithms to identify degradation trends and predict RUL, which can provide flexible and adaptive solutions. Hybrid approaches integrate multiple methods to enhance predictive accuracy by combining the physical insights of model-based methods with the statistical and analytical strengths of data-driven techniques. This paper thoroughly evaluates these methodologies, focusing on recent advancements along with their respective strengths and limitations. By consolidating current findings and highlighting potential pathways for advancement, this review paper serves as a foundational resource for researchers and practitioners working to advance battery health monitoring and RUL prediction methods across both academic and industrial fields.

The increasing demand for reliable and safe Lithium-ion (Li-ion) batteries requires more accurate estimation of state of health (SOH) and remaining useful life (RUL) prediction. However, the inherent complexity and non-linear dynamics of Li-ion batteries present specific challenges to traditional methods of SOH modeling. Although particle filter (PF) techniques can handle nonlinear dynamics, they still face challenges, including particle degeneracy and loss of diversity, that reduce their ability to effectively model the nonlinear degradation mechanisms of batteries. To tackle these limitations, this paper presents a novel artificial intelligence-driven PF (AI-PF) technology for battery health modeling and prognosis. The main contributions of the AI-PF technique are as follows: (1) A novel dynamic sample degeneracy detection method is proposed to provide real-time assessment of particle weights so as to promptly identify degeneracy and improve computational efficiency. (2) An adaptive crossover and mutation strategy is proposed to reallocate low-weight particles and maintain particle diversity to improve modeling and RUL forecasting accuracy. The effectiveness of the AI-PF framework is validated through systematic evaluations carried out using benchmark models and well-recognized battery datasets.