Enhanced Fault Diagnosis Research Articles

Enhanced fault diagnosis of segmented asymmetric stochastic resonance in rotating machinery under strong noise environment

Enhanced fault diagnosis of segmented asymmetric stochastic resonance in rotating machinery under strong noise environment

KAN-HyperMP: An Enhanced Fault Diagnosis Model for Rolling Bearings in Noisy Environments

Rolling bearings often produce non-stationary signals that are easily obscured by noise, particularly in high-noise environments, making fault detection a challenging task. To address this challenge, a novel fault diagnosis approach based on the Kolmogorov–Arnold Network-based Hypergraph Message Passing (KAN-HyperMP) model is proposed. The KAN-HyperMP model is composed of three key components: a neighbor feature aggregation block, a feature fusion block, and a KANLinear block. Firstly, the neighbor feature aggregation block leverages hypergraph theory to integrate information from more distant neighbors, aiding in the reduction of noise impact, even when nearby neighbors are severely affected. Subsequently, the feature fusion block combines the features of these higher-order neighbors with the target node’s own features, enabling the model to capture the complete structure of the hypergraph. Finally, the smoothness properties of B-spline functions within the Kolmogorov–Arnold Network (KAN) are employed to extract critical diagnostic features from noisy signals. The proposed model is trained and evaluated on the Southeast University (SEU) and Jiangnan University (JNU) Datasets, achieving accuracy rates of 99.70% and 99.10%, respectively, demonstrating its effectiveness in fault diagnosis under both noise-free and noisy conditions.

Enhanced fault diagnosis of rolling bearings using an improved inception-lstm network

ABSTRACT Rolling bearings are integral to the operation of various mechanical systems, where their condition directly impacts equipment reliability and performance. Accurate fault diagnosis is essential for preventing unexpected failures and minimising downtime in industrial settings. This study presents a novel diagnostic model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, enhanced by an improved Inception module. This integrated approach captures both spatial and temporal features of vibration signals, leading to superior diagnostic accuracy under diverse operating conditions. Experimental results using the Case Western Reserve University (CWRU) bearing dataset demonstrate an impressive average fault diagnosis accuracy of 99.83%, outperforming conventional models. The robustness of the proposed model against noise and its adaptability to varying operational environments underscore its practical value for real-world engineering applications, offering a reliable solution for maintaining the safety and efficiency of critical machinery.

Physics-informed probabilistic deep network with interpretable mechanism for trustworthy mechanical fault diagnosis

The application of data-driven models based on the neural network is pivotal to developing an intelligent fault diagnostic flowchart. However, the reliability and interpretability of these models for fault prediction have presented challenges in neural network-based diagnostic approach. Another challenge is data availability, which has also been a limiting factor in using artificial Intelligence for condition monitoring and fault assessment in industrial mechanical systems. Consequently, this study proposes a Physics Informed Probabilistic Deep Network (PIPDN) framework to overcome these challenges. The PIPDN comprises two main components: the physical labelling module, designed to enhance physical labels with mechanical failure information, and the main body of PIPDN, responsible for learning fault representative features and generating smart data guided by conditional and physical labels. Furthermore, a multi-scale PIPDN model is developed to integrate the proposed uncertainty quantification (UQ) with decision-fusion module for accurate interpretation and enhanced fault diagnosis. The applicability, effectiveness, and superiority of the proposed framework and approach are validated using an experimental bearing dataset. The results indicate that integrating physical labels significantly assists the PIPDN model in capturing more accurate fault characteristics. This increases the importance of latent space features for subsequent fault diagnosis and also enhances the diagnostic interpretability. Furthermore, the addition of UQ-based decision-making module improves the reliability of the MS-PIPDN model by reducing epistemic uncertainty in the predictions.

Enhanced Fault Diagnosis in IoT: Uniting Data Fusion with Deep Multi-Scale Fusion Neural Network

One of the biggest challenges is figuring out when industrial IoT devices break. Notwithstanding these difficulties, one of the many advantages of using real-time sensor data from the Industrial Internet of Things (IIoT) is the ability to monitor events and respond promptly. The IIoT's sensor numbers may be analysed, combined with data from other sources, and applied quickly to make decisions. This manuscript proposes Enhanced Fault Diagnosis in IoT, Uniting Data Fusion with Deep Multi-Scale Fusion Neural Network (FD-IoT-DMSFNN). Initially, input sensor data are taken from the CWRU Dataset. Then, sensor data is normalised using Multivariate Fast Iterative Filtering. Then, the normalised sensor data are extensively dispersed and diverse; hence, the data outlier detection is performed using the Deep isolation forest (DIF) approach. Therefore, this manuscript proposes a Mexican Axolotl Optimization (MAO) for tuning the weight parameter of DMSFNN, which exhibits an enhanced fault detection process. Python is used to implement the recommended strategy. The proposed method's performance yields a lower completion time of 6.5%, 1.8%, and 4.13%; higher prediction accuracy of 2.26%, 6.71%, and 8.32% compared with existing approaches such as towards deep domain adaptability training methods for industrial IoT edge device soft real-time fault diagnosis (FD-IoT-LSTM), Intelligent Fault Quantitative Identification for the Industrial Internet of Things (IIoT) utilising a particular deep dual reinforcement learning model with insufficient samples (FD-IoT-DRL) and IIoT- based fault identification model for industrial use (FD-IoT-ML-LSTM) respectively.

Cross-domain zero-shot learning for enhanced fault diagnosis in high-voltage circuit breakers

Ensuring the stability of high-voltage circuit breakers (HVCBs) is crucial for maintaining an uninterrupted supply of electricity. Existing fault diagnosis methods typically rely on extensive labeled datasets, which are challenging to obtain due to the unique operational contexts and complex mechanical structures of HVCBs. Additionally, these methods often cater to specific HVCB models and lack generalizability across different types, limiting their practical applicability. To address these challenges, we propose a novel cross-domain zero-shot learning (CDZSL) approach specifically designed for HVCB fault diagnosis. This approach incorporates an adaptive weighted fusion strategy that combines vibration and current signals. To bypass the constraints of manual fault semantics, we develop an automatic semantic construction method. Furthermore, a multi-channel residual convolutional neural network is engineered to distill deep, low-level features, ensuring robust cross-domain diagnostic capabilities. Our model is further enhanced with a local subspace embedding technique that effectively aligns semantic features within the embedding space. Comprehensive experimental evaluations demonstrate the superior performance of our CDZSL approach in diagnosing faults across various HVCB types.

Adaptive EKF enhanced fault diagnosis and fault tolerant control for space manipulators with position measurements only

Space manipulators play a critical role in the operation of space robots. However, the failure of these manipulators can affect the control performance and even lead to instability. To improve the safety and robustness of space manipulators, this paper presents an efficient fault diagnosis and fault tolerant control method by integrating adaptive extended Kalman filter (AEKF) and sliding mode control (SMC). The proposed method uses AEKF for system estimation, taking into account uncertainties in the process and measurement noise, as well as sensor limitations. By detecting the failure of angle sensors timely, the trajectory tracking performance can be guaranteed by the robustness of AEKF. In cases where a joint experiences free-swinging, the failure can be identified based on the estimated control effectiveness coefficients from AEKF. Then, once the failure is identified, the joint angle can be regulated by exploiting the motion coupling between the joints. Furthermore, SMC is employed to mitigate the effects of unknown disturbances in the joints. The effectiveness of the proposed method is demonstrated through numerical simulation, which illustrates its ability to diagnose failures and maintain control performance under various failure scenarios. In addition, the robustness of the method is verified through Monte Carlo simulation, demonstrating its reliability in dealing with various uncertainties.

Power transformer fault diagnosis using dynamic multiscale graph modeling and M2SGCN network based on statistical fusion

Power equipment fault diagnostics hold significant importance for the stability of power grid systems. In pursuit of this objective, this paper proposes a fault diagnosis method that utilizes dynamic multiscale graph (DMG) modeling and the multiscale multi-stream GCN(M2SGCN) network, incorporating statistical fusion. Specifically, a novel DMG modeling method is proposed to derive visibility graph data and horizontal visibility graph data from vibration signals across multiple scales. Next, a comprehensive neural network architecture named M2SGCN is established to learn global and local features simultaneously, providing a more precise representation. Subsequently, a Dempster Shafer evidence theory statistical fusion technique combined with an adaptive threshold model (DSTFusion) is utilized to integrate primary decision results for enhanced fault diagnosis accuracy. In addition, two datasets obtained from single-phase and three-phase power transformers are analyzed to demonstrate the evolution process. When compared to state-of-the-art indicators such as accuracy, precision, recall, and F1 scores, the method proposed excels in multiple aspects, successfully detecting fault states before their occurrence and achieving outstanding performance.

Improved SE-ResNet Acoustic–Vibration Fusion for Rolling Bearing Composite Fault Diagnosis

An enhanced fault diagnosis approach for rolling bearings with composite faults using an optimized Squeeze and Excitation ResNet (SE-ResNet) model is proposed. This method integrates grid search (GS), support vector regression (SVR), ensemble empirical mode decomposition (EEMD), and low-rank multimodal fusion (LMF) to effectively handle the signals of acoustic–vibration fusion. By combining these techniques, the aim is to improve the accuracy and reliability of rolling bearing fault diagnosis. Firstly, improved EEMD combined with GS-SVR and a window function is used for rolling bearing vibration signal decomposition. Singular value methods are used to filter and reconstruct the results. Secondly, Markov transition fields (MTFs) are used to encode vibration signals into 2D images. LMF is used for the fusion of vibration and sound signals. An improved Squeeze and Excitation ResNet50 network is proposed for feature identification and classification of rolling bearing composite fault data. Finally, the method undergoes rigorous testing and evaluation using rolling bearing data. The experimental outcomes demonstrate that, in comparison to traditional neural networks, the enhanced SE-ResNet, integrated with GS-SVR-EEMD and LMF, attains superior diagnostic accuracy. Additionally, the proposed approach can be effectively utilized for diagnosing rolling bearing composite faults.

Virtual Reality Solutions for Enhanced Fault Diagnosis of Rolling Bearings in Passenger Cableways with an Improved Deep Learning Approach

Virtual Reality Solutions for Enhanced Fault Diagnosis of Rolling Bearings in Passenger Cableways with an Improved Deep Learning Approach

FPGA implementation of an improved envelope detection approach for bearing fault diagnosis

This study presents an enhanced envelope detection technique implemented on a field-programmable gate array (FPGA) to diagnose bearing faults in rotating machinery. Bearing faults frequently result in machinery breakdowns, incurring substantial downtime and maintenance expenses. In our approach, we employ the Teager energy operator (TEO) to extract the vibration signal envelope. Subsequently, we subject the envelope signal to the fast Fourier transform (FFT) to generate the envelope spectrum of the vibration signal. Finally, we further refine the envelope spectrum using TEO for a second time, resulting in a pronounced fault peak that facilitates early fault detection. We evaluate the effectiveness and performance of the proposed method using two distinct types of bearing vibration signals, one being simulated and the other measured. Our findings reveal that the suggested approach outperforms traditional envelope detection methods, leading to a substantially enhanced fault diagnosis capability. For instance, when we assess the characteristic frequency ratio (FCFR) for faults in the inner and outer rings of the bearing using the proposed method, we observe that the FCFR values are significantly elevated, ranging from 160 to 330% higher compared to the analysis performed by the TEO and HT methods. Consequently, this indicates that the proposed approach has the ability to detect faults at an earlier stage than other methods. Furthermore, the FPGA-based implementation makes it suitable for critical industrial applications where rapid fault detection is essential to prevent catastrophic failures.

Enhanced fault diagnosis via stochastic resonance in a piecewise asymmetric bistable system.

Weak fault signals are often overwhelmed by strong noise or interference. The key issue in fault diagnosis is to accurately extract useful fault characteristics. Stochastic resonance is an important signal processing method that utilizes noise to enhance weak signals. In this paper, to address the issues of output saturation and imperfect optimization of potential structure models in classical bistable stochastic resonance (CBSR), we propose a piecewise asymmetric stochastic resonance system. A two-state model is used to theoretically derive the output signal-to-noise ratio (SNR) of the bistable system under harmonic excitations, which is compared with the SNR of CBSR to demonstrate the superiority of the method. The method is then applied to fault data. The results indicate that it can achieve a higher output SNR and higher spectral peaks at fault characteristic frequencies/orders, regardless of whether the system operates under fixed or time-varying speed conditions. This study provides new ideas and theoretical guidance for improving the accuracy and reliability of fault diagnosis technology.

Enhanced Fault Diagnosis in Motor Bearings: Leveraging Optimized Wavelet Transform and Non-Local Attention

Enhanced Fault Diagnosis in Motor Bearings: Leveraging Optimized Wavelet Transform and Non-Local Attention

An enhanced fault diagnosis method for fuel cell system using a kernel extreme learning machine optimized with improved sparrow search algorithm

Proton exchange membrane fuel cells (PEMFC) have a broad development prospect in the fields of vehicles, drones and ships due to their high efficiency and cleanliness. However, the problems of insufficient reliability and durability have severely restricted their industrialization process. To improve the safety, reliability and durability of fuel cell system, a fault diagnosis method that combined kernel principal component analysis (KPCA) with an improved sparrow search algorithm (ISSA) and an optimized kernel extreme learning machine (KELM) was proposed in this study. Firstly, KPCA is utilized to extract nonlinear features from fault indicators and obtain the fault feature vector of the fuel cell system. Then, by incorporating logistic mapping and Cauchy Gaussian mutation strategies to improve the Sparrow Search Algorithm (SSA), ISSA was used to optimize the kernel parameters and regularization coefficient in KELM. The experimental results show that the KPCA-ISSA-KELM method for normal conditions, hydrogen leakage and membrane drying are 100%, 98.5% and 100%, respectively, with an overall accuracy of 99.5% and an operation time of 0.97s. The diagnostic accuracy of the proposed method is 10.4%, 5.7%, 4.8%, 4.2%, 3.0%, 1.8% higher than support vector machine (SVM), back propagation neural network (BPNN), KELM, genetic algorithm-based KELM (GA-KELM), particle swarm optimization-based KELM (PSO-KELM) and SSA-KELM, respectively, and the operation time is only slightly higher than that of the SVM model and KELM model.

Deep Learning for Enhanced Fault Diagnosis of Monoblock Centrifugal Pumps: Spectrogram-Based Analysis

The reliable operation of monoblock centrifugal pumps (MCP) is crucial in various industrial applications. Achieving optimal performance and minimizing costly downtime requires effectively detecting and diagnosing faults in critical pump components. This study proposes an innovative approach that leverages deep transfer learning techniques. An accelerometer was adopted to capture vibration signals emitted by the pump. These signals are then converted into spectrogram images which serve as the input for a sophisticated classification system based on deep learning. This enables the accurate identification and diagnosis of pump faults. To evaluate the effectiveness of the proposed methodology, 15 pre-trained networks including ResNet-50, InceptionV3, GoogLeNet, DenseNet-201, ShuffleNet, VGG-19, MobileNet-v2, InceptionResNetV2, VGG-16, NasNetmobile, EfficientNetb0, AlexNet, ResNet-18, Xception, ResNet101 and ResNet-18 were employed. The experimental results demonstrate the efficacy of the proposed approach with AlexNet exhibiting the highest level of accuracy among the pre-trained networks. Additionally, a meticulous evaluation of the execution time of the classification process was performed. AlexNet achieved 100.00% accuracy with an impressive execution (training) time of 17 s. This research provides invaluable insights into applying deep transfer learning for fault detection and diagnosis in MCP. Using pre-trained networks offers an efficient and precise solution for this task. The findings of this study have the potential to significantly enhance the reliability and maintenance practices of MCP in various industrial settings.

Vibration amplitude normalization enhanced fault diagnosis under conditions of variable speed and extremely limited samples

Intelligent fault diagnosis of rotating equipment is increasingly reliant on algorithms that are driven by big data. By contrast, signal processing was once widely utilized for fault diagnosis in machinery as a classical tool for signal analysis due to its capability to investigate the fault-related mechanism and almost no demand on the number of data samples. This investigation was motivated by the notion that signal processing and data-driven algorithms are combined to exploit their respective characteristics and strengths. Furthermore, in engineering practice, numerous complex factors such as time-variable operating conditions of equipment, non-stationary properties of signals, and extremely limited samples available for model training, can make it difficult to learn discriminative features from input data, thereby diminishing the diagnostic accuracy. In this paper, a novel framework of vibration amplitude normalization (VAN) enhanced fault diagnosis is proposed. Firstly, after dissects deeply the effects of the time-varying speed conditions on vibration signal and its characteristics, VAN technique is proposed for non-stationary signal processing to obtain the approximate stationary signal, so as to facilitate the subsequent state characteristics mining from the vibration signal. Then, two VAN enhanced fault diagnosis methods—i.e. signal amplitude normalization integrated with shallow learning by cascade and VAN integrated with deep learning by embedding—are developed to capture discriminative features from approximate stationary signal for fault diagnosis under conditions of variable speed and extremely limited samples. Finally, the feasibility and effectiveness of the proposed methods are verified using actual vibration datasets measured on test rig and in-site wind turbines. The number of samples required to achieve the same diagnostic accuracy is reduced by an average of 60%, demonstrating the superiority.

Digital twin enhanced fault diagnosis reasoning for autoclave

Digital twin enhanced fault diagnosis reasoning for autoclave

Enhanced fault diagnosis of wind energy conversion systems using ensemble learning based on sine cosine algorithm

This paper investigates the problem of incipient fault detection and diagnosis (FDD) in wind energy conversion systems (WECS) using an innovative and effective approach called the ensemble learning-sine cosine optimization algorithm (EL-SCOA). The evolved strategy involves two primary steps: first, a sine-cosine algorithm is used to extract and optimize features in order to only select the most descriptive ones. Second, to further improve the capability, thereby providing the highest accuracy performance, the newly gathered dataset is introduced as input to an ensemble learning paradigm, which merges the benefits of boosting and bagging techniques with an artificial neural network classifier. The essential goal of the developed proposal is to discriminate between the diverse operating conditions (one healthy and six faulty conditions). Three potential and frequent types of faults that can affect the system behaviors including short-circuit, open-circuit, and wear-out are considered and thereby injected at diverse locations and sides (grid and generator sides) in order to evaluate the availability and performance of the proposed technique when compared to the conventional FDD methods. The diagnosis performance is analyzed in terms of accuracy, recall, precision, and computation time. The acquired outcomes demonstrate the efficiency of the suggested diagnostic paradigm compared to conventional FDD techniques (accuracy rate has been successfully achieved 98.35%).

Digital twin-driven fault diagnosis method for composite faults by combining virtual and real data

The subsea production system is essential for the subsea production of oil and gas. Real-time monitoring can ensure safe production. The subsea production control system is the core of the subsea production system and the top priority to be monitored. Troubleshooting the subsea production system involves lifting the component to the platform to repair it. And then send it back subsea. There is no doubt that it is expensive. In this case, some faults will not be handled temporarily if they do not affect the system operation seriously. This leads to multiple faults existing simultaneously, resulting in mixed signals. That makes it difficult to distinguish the state of the subsea production system. Thus, a fault diagnosis method for composite faults is needed. A digital twin-driven fault diagnosis method for composite faults is proposed by combining virtual and real data. Bernoulli Equation combines loss, control, and state parameters to build a digital twin model. A cross-validation enhanced fault diagnosis method is applied to single faults. A Bayesian network-based fault diagnosis model combining virtual and real data is built for composite faults. Field data from an offshore platform in the South Sea of China is used to demonstrate the effect of the proposed method. The results indicate that the method is extremely effective for composite faults of the subsea production control system.

Fault diagnosis method of critical industrial equipment based on knowledge graphs and multi-task learning

针对工业生产关键设备故障数据稀疏的问题和故障诊断的需求，本文提出了一种基于知识图谱和多任务学习的工业生产关键设备故障诊断模型MKFD(Multi-task learning for Knowledge graph enhanced Fault Diagnosis)，通过对故障根因的推断实现故障诊断。设计了多任务学习框架，并构造了一种改进十字绣单元用于实现框架内子任务之间的信息共享。利用运维数据构建故障现象-故障根因关联矩阵，使用多层感知机搭建知识图谱嵌入模型；分别将关联矩阵嵌入和知识图谱嵌入作为多任务学习框架中的两个子任务，通过子任务的交替学习，优化MKFD模型参数，实现对故障根因的推断，从而达到故障诊断的目的。最后，基于国内某工业企业的运维数据所构建的两个工业生产关键设备故障知识图谱对上述方案进行了验证实验，结果证明所提出的方法具有良好的性能。