Search Results (228)

Fault diagnosis methods based on deep learning have been widely applied to bearing fault diagnosis. However, current methods usually diagnose on the same individual device, which cannot guarantee reliability in real industrial scenarios, especially for new individual devices. This article explores a practical cross-individual scenario and proposes a Kolmogorov–Arnold enhanced convolutional transformer (KACFormer) model to improve both general feature representation and cross-individual capabilities. Specifically, the Kolmogorov–Arnold representation theorem is embedded into convolution and multi-head attention mechanisms to develop novel Kolmogorov–Arnold enhanced convolution (KAConv) and Kolmogorov–Arnold enhanced attention (KAA). The adaptive activation function enhances its nonlinear modeling ability. Comprehensive experiments are performed on two public datasets, demonstrating the superior generalization of the proposed KACFormer model with a higher accuracy of 95.73% and 91.58% compared to existing advanced models. Full article

Oil condition monitoring can ensure the safe operation of mechanical equipment. Metal debris is full of friction information, and the identification of debris material helps to locate wear of parts. A method based on impedance analysis is proposed to identify debris material in this article. The differences in permeability and conductivity result in the nonlinear variation trend of inductance–resistance amplitude with debris volume. By establishing a database of amplitude–size curves, debris information (material and size) can be obtained through impedance analysis. Based on experimental and simulation results, iron, stainless steel, aluminum, copper, and brass particles are effectively distinguished. This method is not affected by oil’s light transmittance, other impurities, and debris surface dirt and can be used to distinguish metals with similar colors. This work provides a novel solution for debris material identification, which is expected to promote the development of fault diagnosis. Full article

As a core component of machining tools and vehicles, the load-bearing and transmission performance of rolling bearings is directly related to product processing quality and driving safety, highlighting the critical importance of fault detection. To address the nonlinearity, non-stationary modulation, and low signal-to-noise ratio (SNR) observed in bearing vibration signals, we propose a fault feature extraction method based on spectral kurtosis and Hilbert envelope demodulation. First, spectral kurtosis is employed to determine the center frequency and bandwidth of the signal adaptively, and a bandpass filter is constructed to enhance the characteristic frequency components. Subsequently, the envelope spectrum is extracted through the Hilbert transform, allowing for the precise identification of fault characteristic frequencies. In the fault diagnosis stage, a multidimensional feature vector is formed by combining the kurtosis index with the amplitude ratios of inner/outer race characteristic frequencies, and fault pattern classification is accomplished using a Least-Squares Support Vector Machine (LS-SVM). To evaluate the effectiveness of the proposed method, experiments were conducted on the bearing datasets from Case Western Reserve University (CWRU) and the Machine Failure Prevention Technology (MFPT) Society. The experimental results demonstrate that the proposed method surpasses other comparative approaches, achieving identification accuracies of 95% and 100% for the CWRU and MFPT datasets, respectively. Full article

Failures in gas path components pose significant risks to aeroengine performance and safety. Traditional fault diagnosis methods often require extensive data and struggle with real-time applications. This study addresses these critical limitations in traditional studies through physics-informed modeling and adaptive estimation. A nonlinear component-level model of the JT9D engine is developed through aero-thermodynamic governing equations, enhanced by a dual-loop iterative cycle combining Newton–Raphson steady-state resolution with integration-based dynamic convergence. An augmented state-space model that linearizes nonlinear dynamic models while incorporating gas path health characteristics as control inputs is novelly proposed, supported by similarity-criterion normalization to mitigate matrix ill-conditioning. A hybrid identification algorithm is proposed, synergizing partial derivative analysis with least squares fitting, which uniquely combines non-iterative perturbation advantages with high-precision least squares. This paper proposes a novel enhanced Kalman filter through integral compensation and three-dimensional interpolation, enabling real-time parameter updates across flight envelopes. The experimental results demonstrate a 0.714–2.953% RMSE in fault diagnosis performance, a 3.619% accuracy enhancement over traditional sliding mode observer algorithms, and 2.11 s reduction in settling time, eliminating noise accumulation. The model maintains dynamic trend consistency and steady-state accuracy with errors of 0.482–0.039%. This work shows marked improvements in temporal resolution, diagnostic accuracy, and flight envelope adaptability compared to conventional approaches. Full article

Axial piston pumps play a vital role in fluid power systems, which are widely employed in diverse fields such as aerospace, ocean engineering, and rail transit. It is essential to accurately diagnose faults in these pumps since their reliable operation hinges on it. A graph isomorphic network with a spatio-temporal attention mechanism (GIN-ST) is proposed in this paper for fault diagnosis of hydraulic axial piston pumps; GIN-AM addresses the problem of traditional intelligent fault diagnosis methods being limited to nonlinear mapping and transformation in Euclidean space. Initially, the weighted graphs are constructed from a univariate time series through K-nearest neighbor graph methods. Subsequently, a spatio-temporal attention-based module used to learn the graph representation of piston pump faults is presented, where a novel READOUT function and Transformer encoder provide spatial and temporal interpretability, respectively. Finally, the proposed (GIN-ST) model is compared against other intelligent fault diagnosis methods, and the superiority of the proposed method is proven. Full article

As a critical component of rotating machinery, the operational status of rolling bearings is considered to directly determine the reliability of rail traffic systems. To address the complex modulation effects existing between multiple bearing components and the non-linear, non-stationary characteristics exhibited by vibration acceleration signals, an intelligent fault diagnosis method for bearings based on Hilbert envelope demodulation and Ensemble Empirical Mode Decomposition energy distribution features is proposed. First, the original vibration signal is subjected to envelope demodulation processing by the Hilbert transform, thereby effectively separating the envelope signal containing fault characteristics. Subsequently, the demodulated envelope signal is decomposed by EEMD to extract Intrinsic Mode Functions (IMFs), where each IMF component is calculated layer by layer using a normalization method based on the EEMD decomposition sequence. Finally, the proposed algorithm is validated by the standard bearing fault dataset from Case Western Reserve University. Experimental results show that the proposed method achieves 100% accuracy in fault identification, and its superiority is proven to exceed conventional diagnostic approaches significantly. Full article

Lithium-ion batteries experience nonlinear degradation characteristics during long-term operation. Accurate estimation of their remaining useful life (RUL) is of significant importance for early fault diagnosis and residual value evaluation. However, existing RUL prediction approaches often suffer from limited accuracy and insufficient specificity. To address these limitations, this study proposes an RUL prediction methodology based on Gaussian process regression, which incorporates degradation pattern recognition and auxiliary features derived from knee points. First, 9 health-related features are extracted from the first 100 charge/discharge cycles of the battery. Based on these extracted features, clustering and classification techniques are employed to categorize the batteries into three distinct degradation patterns. Moreover, feature importance is assessed to identify and eliminate redundant indicators, thereby enhancing the relevance of the feature set for prediction. Subsequently, for each degradation pattern, GPR-based models with composite kernel functions are constructed by integrating knee point positions and their corresponding slopes. The model hyperparameters are further optimized through the particle swarm optimization (PSO) algorithm to improve the adaptability and generalization capability of the predictive models. Experimental results demonstrate that the proposed method achieves a high level of predictive accuracy, with an overall mean absolute percentage error (MAPE) of approximately 8.70%. Furthermore, compared with conventional prediction methods, the proposed approach exhibits superior performance and can serve as an effective evaluation tool for diverse application scenarios, including lithium-ion battery health monitoring, early prognostics, and echelon utilization. Full article

Aiming at the limitations of existing multisensor fault diagnosis methods for rolling bearings in real industrial scenarios, this paper proposes an innovative intuitionistic fuzzy weighted least squares twin support higher-order tensor machine (IFW-LSTSHTM) model, which realizes a breakthrough in the noise robustness, adaptability to the working conditions, and the class imbalance processing capability. First, the multimodal feature tensor is constructed: the fourier synchro-squeezed transform is used to convert the multisensor time-domain signals into time–frequency images, and then the tensor is reconstructed to retain the three-dimensional structural information of the sensor coupling relationship and time–frequency features. The nonlinear feature mapping strategy combined with Tucker decomposition effectively maintains the high-order correlation of the feature tensor. Second, the adaptive sample-weighting mechanism is developed: an intuitionistic fuzzy membership score assignment scheme with global–local information fusion is proposed. At the global level, the class contribution is assessed based on the relative position of the samples to the classification boundary; at the local level, the topological structural features of the sample distribution are captured by K-nearest neighbor analysis; this mechanism significantly improves the recognition of noisy samples and the handling of class-imbalanced data. Finally, a dual hyperplane classifier is constructed in tensor space: a structural risk regularization term is introduced to enhance the model generalization ability and a dynamic penalty factor is set to set adaptive weights for different categories. A linear equation system solving strategy is adopted: the nonparallel hyperplane optimization is converted into matrix operations to improve the computational efficiency. The extensive experimental results on the two rolling bearing datasets have verified that the proposed method outperforms existing solutions in diagnostic accuracy and stability. Full article

The evidential reasoning (ER) rule has been widely adopted in engineering fault diagnosis, yet its conventional implementations inherently neglect evidence correlations due to the foundational independence assumption required for Bayesian inference. This limitation becomes particularly critical in practical scenarios where heterogeneous evidence collected from diverse sensor types exhibits significant correlations. Existing correlation processing methods fail to comprehensively address both linear and nonlinear correlations inherent in such heterogeneous evidence systems. To resolve these theoretical and practical constraints, this study develops MICER—a novel ER framework that incorporates correlation analysis based on the maximum mutual information coefficient (MIC). The proposed methodology advances ER theory by systematically integrating evidence interdependencies, thereby expanding both the theoretical boundaries of ER rules and their applicability in real-world fault diagnosis. Flange ring loosening fault diagnosis and flywheel system fault diagnosis cases are experimentally verified and the effectiveness of the method is demonstrated. Full article

Fatigue cracks in blades pose a significant threat to the safe operation of rotating machinery. Currently, the application of non-contact displacement sensors in blade vibration measurements has enabled the widespread analysis of nonlinear dynamic characteristics, such as natural frequency deviations and spectral anomalies, to enhance crack fault diagnosis in rotating machinery. However, these two dynamic characteristics are not distinguishable for crack changes, especially for incipient cracks, leading to potential misdiagnosis. In this paper, a dynamic characteristic called the envelope diagram image of vibration responses (EDIVR) was extracted from blade tip displacement signals collected during acceleration–deceleration cycles for crack diagnosis. Initially, considering the breathing effect of fatigue cracks, a structural dynamics finite element model of a blade containing a breathing crack is established to calculate its dynamic response under aerodynamic force. Subsequently, the sensitivity of three characteristics (natural frequency, frequency spectrum, and EDIVR) to crack fault changes is quantitatively compared based on the simulated response signals. Experimental validation confirms the accuracy of the proposed dynamic model and the effectiveness of the proposed feature. The study shows that under identical operational conditions, blades with cracks of equivalent depth and location exhibit maximum sensitivity to crack detection when EDIVR dynamic characteristics are employed as the fault diagnostic criterion. Moreover, this characteristic is less susceptible to signal noise interference compared to other dynamic characteristics, enhancing its potential for crack diagnosis in engineering applications. Full article

To address the issue of control inaccuracy caused by the zero-offset fault in current sensors within the multiphase brushless DC motor (BLDCM) drive system, this paper proposes a fault diagnosis and fault-tolerant control method based on current signals. Different from traditional solutions that rely on hardware redundancy or precise modeling, this method constructs a dual-channel fault diagnosis framework by integrating the steady-state amplitude offset of the phase current after the fault and the abnormal characteristics of dynamic sector switching. Firstly, sliding time window monitoring is used to identify steady-state amplitude anomalies and locate faulty sectors. Subsequently, an algorithm for detecting the difference in current changes during sector switching is designed, and a logic interlocking verification mechanism is combined to eliminate false triggering and accurately locate single or multiple fault phases. Furthermore, based on the diagnostic information, a repeated iterative online correction method is adopted to restore the accuracy of the current measurement. This method only relies on phase current signals and rotor position information, does not require additional hardware support or accurate system models, and is not affected by the nonlinear characteristics of the motor. Finally, the experimental verification was carried out on a nine-phase BLDCM drive system. Experimental results indicate that the torque fluctuation of the system can be controlled within 5% through the fault-tolerant control strategy. Full article

With the rapid development of new energy vehicle technologies, higher demands have been placed on fault diagnosis for automotive transmission gearboxes. To address the poor adaptability of traditional methods under complex operating conditions, this paper proposes a sensor data-driven fault diagnosis method based on improved ensemble empirical mode decomposition (EEMD) combined with convolutional neural networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The method incorporates a dynamic noise adjustment mechanism, allowing the noise amplitude to adapt to the characteristics of the signal. This improves the stability and accuracy of signal decomposition, effectively reducing the instability and error accumulation associated with fixed-amplitude white noise in traditional EEMD. By combining the CNN and BiLSTM modules, the approach achieves efficient feature extraction and dynamic modeling. First, vibration signals of the transmission gearbox under different operating states are collected via sensors, and an improved EEMD method is employed to decompose the signals, removing background noise and nonstationary components to extract diagnostically significant intrinsic mode functions (IMFs). Then, the CNN is utilized to extract features from the IMFs, deeply mining their spatiotemporal characteristics, while the BiLSTM captures the temporal sequence dependencies of the signals, enhancing the comprehensive modeling of nonlinear and dynamic fault features. The combination of these two networks enables efficient adaptation to complex conditions, achieving accurate classification and identification of multiple gearbox fault modes. Results indicate that the proposed approach is highly accurate and robust for identifying gearbox fault modes, significantly exceeding the performance of conventional methods and isolated network models. This provides an efficient and intelligent solution for fault diagnosis of automotive transmission gearboxes. Full article

Precision rolling bearings serve as critical components in a range of diverse industrial applications, where their continuous health monitoring is essential for preventing costly downtime and catastrophic failures. Early-stage bearing defects present significant diagnostic challenges, as they manifest as weak, nonlinear, and non-stationary transient features embedded within high-amplitude random noise. While entropy-based methods have evolved substantially since Shannon’s pioneering work—from approximate entropy to multiscale variants—existing approaches continue to face limitations in their computational efficiency and information preservation. This paper introduces the Adaptive Composite Multiscale Slope Entropy (ACMSlE) framework, which overcomes these constraints through two innovative mechanisms: a time-window shifting strategy, generating overlapping coarse-grained sequences that preserve critical signal information traditionally lost in non-overlapping segmentation, and an adaptive scale optimization algorithm that dynamically selects discriminative scales through entropy variation coefficients. In a comparative analysis against recent innovations, our integrated fault diagnosis framework—combining Fast Ensemble Empirical Mode Decomposition (FEEMD) preprocessing with Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM) classification—achieves 98.7% diagnostic accuracy across multiple bearing defect types and operating conditions. Comprehensive validation through a multidimensional stability analysis, complexity discrimination testing, and data sensitivity analysis confirms this framework’s robust fault separation capability. Full article

Since permanent magnets and rotors are core components of electric vehicle drive motors, accurate diagnosis of demagnetization and eccentricity faults is crucial for ensuring the safe operation of electric vehicles. Currently, intelligent diagnostic methods based on three-phase current signals have been widely adopted due to their advantages of easy acquisition, low cost, and non-invasiveness. However, in practical applications, the fault characteristics in current signals are relatively weak, leading to diagnostic performance that falls short of expected standards. To address this issue and improve diagnostic accuracy, this paper proposes a novel diagnostic method. First, branch current is utilized as the data source for diagnosis to enhance the fault characteristics of the diagnostic signal. Next, a dual-modal feature extraction module is constructed, employing Variational Mode Decomposition (VMD) and Fast Fourier Transform (FFT) to concatenate the input branch current along the feature dimension in both the time and frequency domains, achieving nonlinear coupling of time–frequency features. Finally, to further improve diagnostic accuracy, a cascaded convolutional neural network based on dilated convolutional layers and multi-scale convolutional layers is designed as the diagnostic model. Experimental results show that the method proposed in this paper achieves a diagnostic accuracy of 98.6%, with a misjudgment rate of only about 2% and no overlapping feature results. Compared with existing methods, the method proposed in this paper can extract higher-quality fault features, has better diagnostic accuracy, a lower misjudgment rate, and more excellent feature separation ability, demonstrating great potential in intelligent fault diagnosis and maintenance of electric vehicles. Full article

The global shift toward renewable energy, particularly wind power, underscores the critical need for advanced fault diagnosis systems to optimize wind turbine reliability and efficiency. While traditional diagnostic methods remain foundational, their limitations in addressing the nonlinear dynamics and operational complexity of modern turbines have accelerated the adoption of Artificial Intelligence (AI)-driven approaches. This review systematically examines advancements in AI-based fault diagnosis techniques, including machine learning (ML) and deep learning (DL), from 2019 to 2024, analyzing their evolution, efficacy, and practical challenges. Drawing on a curated selection of 55 studies (identified via structured searches across IEEE Xplore, ScienceDirect, and Web of Science), the paper prioritizes research employing data-driven or model-based methodologies with explicit experimental validation and clearly documented data sources. The excluded works lacked English accessibility, validation, or data transparency. Focusing on high-impact faults in gearboxes, blades, and generators, these components are responsible for over 70% of turbine failures, the review maps prevalent ML and DL algorithms, such as CNNs, LSTMs, and SVMs, to specific fault types, revealing hybrid AI models and real-world data integration as key drivers of diagnostic accuracy. Critical gaps are identified, including overreliance on simulated datasets and inconsistent signal preprocessing, which hinder real-world applicability. This study concludes with actionable recommendations for future research, advocating adaptive noise-filtering techniques, scalable hybrid architectures, and standardized benchmarking using operational turbine data. By bridging theoretical AI advancements with practical deployment challenges, this work aims to inform next-generation fault diagnosis systems, enhancing turbine longevity and supporting global renewable energy goals. Full article