Reliability Of Fault Diagnosis Research Articles

A fault detection of aero-engine rolling bearings based on CNN-BiLSTM network integrated cross-attention

Abstract Aero-engine rolling bearings are essential for engine health, in which disruptive failures can be prevented and reduce great losses in air flight. To improve the efficiency of fault detection, an improved network, named CNN- BiLSTM -Cross-Attention (CBLCA) was proposed. The Bidirectional Long Short-Term Memory (BiLSTM) layer captures the temporal features as the input data. The cross-attention mechanism is integrated with the Convolutional Neural Networks (CNN) layer and the BiLSTM layer respectively. More important feature information can be identified with the CBLCA model. The proposed model was also validated with the open-sourced aero-engine rolling bearings data set. To improve the identification accuracy, a novel method that combines Fast Fourier Transform (FFT) and Variational Mode Decomposition (VMD) is used for the data preprocessing. Each original signal sample is transformed into a feature set containing richer information, and the number of features significantly increased in the entire dataset. Compared with some existing LSTM models, such as LSTM, BiLSTM, CNN-BiLSTM, and CNN-LSTM, the classification accuracy was increased by 55%, 54%, 5%, and 7%, respectively. The processing method for vibration signals and the CBLCA model can improve the accuracy and reliability of fault diagnosis for aero-engine rolling bearings.

DRSwin-ST: An intelligent fault diagnosis framework based on dynamic threshold noise reduction and sparse transformer with Shifted Windows

In real industrial environments, acquiring vibration data from bearings is often challenging due to noise, resulting in network models that excel when trained on datasets with sufficient samples but struggle with accurate fault identification in real-world scenarios, inevitably threatening the reliability of fault diagnosis. To address this problem, this paper proposes an end-to-end fault diagnosis framework (DRSwin-ST) based on sparse transformer with a shift window and dynamic threshold noise reduction. The Swin-Transformer serves as the backbone, leveraging a multi-head self-attention mechanism with a shift window to capture global information. The 1.5-Entmax replaces Softmax in the self-attention mechanism, sparsifying irrelevant information and allowing the model to focus on essential details. The self-attention mechanism, combined with a multi-scale structure, forms a forward feedback network to obtain rich fault feature information. In addition, the paper integrates a large convolutional kernel and a dynamic soft-threshold noise reduction module to construct a convolutional network in front of the transformer structure. This configuration extracts fault feature information and removes the noise, enhancing the fault recognition accuracy of the model. Experimental results on three diverse datasets demonstrate that DRSwin-ST exhibits robustness and high accuracy even in scenarios with limited samples and high noise, validating its exceptional performance.

FCHG: Fuzzy Cognitive Hypergraph for interpretable fault detection

While deep learning-based fault detection methods for rolling body mechanical components have achieved good results, their application in real-world scenarios is limited, and their reliability is not satisfactory due to the non-interpretability of deep learning. Meanwhile, automated component vibration signals are usually affected by irregularities, which make it challenging to obtain advanced features by conventional feature extraction methods. To solve the above problems, we develop a new fuzzy cognitive graph model that combines the interpretability of fuzzy cognitive graphs with the high-precision categorization of hypergraphs, called fuzzy cognitive hypergraph (FCHG). First, frequency domain analysis is combined with graph analysis to help the network extract high-level features. Second, causal learning is used to construct the FCHG weight matrix to make the FCHG interpretable and enhance the reliability of fault diagnosis. Finally, the FCHG network is built using spectrogram theory and Chebyshev polynomial expansion to capture multiple feature relationships and improve fault diagnosis accuracy. The practicality and effectiveness of the FCHG model for monitoring the health and stability status of rolling body mechanical components are verified on three datasets.

Bearing Fault Diagnosis Based on Graph Formulation and Graph Convolutional Network

Bearing fault diagnosis stands as a critical component in the maintenance of rotating machinery. Many prevalent deep learning techniques are tailored to Euclidean datasets such as audio, image, and video. However, these methods falter when confronting non-Euclidean datasets, notably graph representations. In response, here we introduce an innovative approach harnessing the Graph Convolutional Network (GCN) to analyze graph data derived from vibration signals related to bearing faults. This enhances the precision and reliability of fault diagnosis. Our methodology initiates by deriving a periodogram from the unprocessed vibration signals. Subsequently, this periodogram is mapped into a graph format, upon which the GCN is engaged for classification purposes. We substantiate the efficacy of our approach through rigorous experimental assessments conducted on a collection of ten bearing sets. Within these experiments, an accelerometer chronicles vibration signals across varying load conditions. We probe into the diagnostic accuracy rates across diverse loads and Signal-to-Noise Ratios (SNRs). Furthermore, a comparative evaluation of our method against several established algorithms delineated in this study is undertaken. Empirical observations confirm that our GCN-based strategy registers an elevated diagnostic accuracy quotient.

Transfer learning-based multiple digital twin-assisted intelligent mechanical fault diagnosis

With the advancement of complex system diagnosis, prediction, and health management technologies, digital twin technology has become a prominent research area in the fields of intelligent manufacturing and system operation and maintenance. However, due to the high complexity of practical systems, the difficulty of data acquisition, and the low accuracy of modeling techniques, current digital twin modeling suffers from low accuracy, and the generalization ability of models is poor when applied in model transfer. To address this issue, a novel fault diagnosis method is proposed, which integrates a digital twin model based on transfer learning. The framework introduces an innovative approach to construct multiple digital twin models using both mechanistic and data-driven models. The mechanism twin constructs a universal simulation model based on physical equipment and updates it with system response measurement data. The data twin consists of a high-dimensional fully connected-generative adversarial network twin for extracting deep features from data and an long- and short-term memory twin for extracting time series features. Subsequently, transfer learning is introduced to achieve deep fusion in the multiple digital twins system. The mechanism twin is used to obtain source domain samples to construct a diagnostic network, and the data twin is used to extract target domain features to correct the diagnostic network, thereby improving the accuracy and reliability of fault diagnosis. Finally, the proposed framework is applied to the fault diagnosis of triplex pump equipment. The accuracy of diagnosis continuously improves as the system is updated and ultimately reaches 89.28%, demonstrating the effectiveness of the algorithm and providing a novel solution for the generalization limitations of current digital twin models.

Fault diagnosis of marine machinery via an intelligent data-driven framework

Large amounts of ship accidents caused by marine machinery indicate the significance of fault diagnosis for marine machinery. Since numerous onboard sensor signals contain feature information that reflects the status of marine machinery and considering the time-consuming and experience-dependent of previous data-driven fault diagnosis methods, an intelligent data-driven framework based on a multi-head attention neural network (MANet) is proposed for the fault diagnosis of marine machinery in this work. MANet integrates the multi-head attention (MHA) mechanism, convolutional layers, and residual (Res) structure. Therein, the global correlation features in long-term operating signal sequences extracted by MHA can effectively characterize the status variation of marine machinery; the local fault features in operating signals extracted by convolutional layers can effectively characterize status information at certain moments for marine machinery; the application of Res structure in MANet is conducive to alleviating the gradient propagation anomaly. With a case study for the fault diagnosis of marine machinery, the superiority of MANet in terms of the accuracy and reliability of fault diagnosis is validated by comparing it with two recently published methods. Moreover, the results under different cases of engine loads and application scenarios validate that MANet possesses promising generalization performance for fault diagnosis. Hence, the proposed method provides a new approach for the real-time fault diagnosis of marine machinery in engineering applications.

Online surface temperature prediction and abnormal diagnosis of lithium-ion batteries based on hybrid neural network and fault threshold optimization

Online diagnosis of abnormal temperature is vital to ensure the reliability and operation safety of lithium-ion batteries, and this study develops a hybrid neural network and fault threshold optimization algorithm for their online surface temperature prediction and abnormal diagnosis. To be specific, a hybrid neural network incorporating convolutional neural network and long short-term memory neural network is firstly employed to predict the battery temperature, and a residual monitor is designed to track the deviation between the measure and the prediction. Then, the acquired residual is compared with the fault threshold to diagnose whether the battery temperature is abnormal. Moreover, to improve the correctness and reliability of fault diagnosis, a fault threshold optimization algorithm based on the receiver operating characteristic curve is defined to automatically find the optimal fault threshold. The accuracy and reliability of temperature prediction is verified under various aged state and temperatures, as well as different battery types. The abnormal experiment validation on three datasets reveals that the proposed method can diagnose and unwind temperature fault warning timely and reliably. Additionally, the average execution time of each prediction and diagnosis is less than 3.5 ms, manifesting the real-time application capability of the proposed method.

A Novel Hybrid Optimization Approach for Fault Detection in Photovoltaic Arrays and Inverters Using AI and Statistical Learning Techniques: A Focus on Sustainable Environment

Fault detection in PV arrays and inverters is critical for ensuring maximum efficiency and performance. Artificial intelligence (AI) learning can be used to quickly identify issues, resulting in a sustainable environment with reduced downtime and maintenance costs. As the use of solar energy systems continues to grow, the need for reliable and efficient fault detection and diagnosis techniques becomes more critical. This paper presents a novel approach for fault detection in photovoltaic (PV) arrays and inverters, combining AI techniques. It integrates Elman neural network (ENN), boosted tree algorithms (BTA), multi-layer perceptron (MLP), and Gaussian processes regression (GPR) for enhanced accuracy and reliability in fault diagnosis. It leverages its strengths for the accuracy and reliability of fault diagnosis. Feature engineering-based sensitivity analysis was utilized for feature extraction. The fault detection and diagnosis were assessed using several statistical criteria including PBAIS, MAE, NSE, RMSE, and MAPE. Two intelligent learning scenarios are carried out. The first scenario is conducted for PV array fault detection with DC power (DCP) as output. The second scenario is conducted for inverter fault detection with AC power (ACP) as the output. The proposed technique is capable of detecting faults in PV arrays and inverters, providing a reliable solution for enhancing the performance and reliability of solar energy systems. A real-world solar energy dataset is used to evaluate the proposed technique with results compared to existing detection techniques and obtained results showing that it outperforms existing fault detection techniques, achieving higher accuracy and better performance. The GPR-M4 optimization justified its reliably among all the models with MAPE = 0.0393 and MAE = 0.002 for inverter fault detection, and MAPE = 0.091 and MAE = 0.000 for PV array fault detection.

A new method for fault identification of T-connection transmission line based on multi-scale traveling wave reactive power and random forest.

Though the traditional fault diagnosis method of T-connected transmission lines can identify the faults inside and outside the area, it can not identify the specific branches. To improve the accuracy and reliability of fault diagnosis of T-connection transmission lines, a new method is proposed to identify specific faulty branches of T-connection transmission lines based on multi-scale traveling wave reactive power and random forest. Based on the S-transform, the mean and sum ratios of the corresponding short-time series traveling wave reactive powers of each two traveling wave protection units at multiple frequencies are calculated respectively to form the fault feature vector sample set of the T-connection transmission line. A random forest fault branch identification model is established, and it is trained and tested by the fault feature sample set of T-connection transmission line to identify the fault branch. The simulation results show that the proposed algorithm can identify the branch where the fault is located inside and outside the protection zone of T-connection transmission line quickly and accurately under various working conditions. This method also shows good performance to identify faults even under the situation of CT saturation, noise influence and data loss.

Multi-Scale Feature Fusion Convolutional Neural Networks for Fault Diagnosis of Electromechanical Actuator

Airborne electromechanical actuators (EMAs) play a key role in the flight control system, and their health condition has a considerable impact on the flight status and safety of aircraft. Considering the multi-scale feature of fault signals and the fault diagnosis reliability for EMAs under complex working conditions, a novel fault diagnosis method of multi-scale feature fusion convolutional neural network (MSFFCNN) is proposed. Leveraging the multiple different scales’ learning structure and attention mechanism-based feature fusion, the fault-related information can be effectively captured and learned, thereby improving the recognition ability and diagnostic performance of the network. The proposed method was evaluated by experiments and compared with the other three fault-diagnosis algorithms. The results show that the proposed MSFFCNN approach has a better diagnostic performance compared with the state-of-the-art fault diagnosis methods, which demonstrates the effectiveness and superiority of the proposed method.

A data‐driven fault diagnosis of high speed maglev train levitation system

SummaryThe levitation system is a crucial component of a maglev train as it is responsible for levitating the train body above the track. Real‐time diagnosis of faults in the levitation system is essential for commercial operational purposes. Despite extensive research on the mechanism of the levitation system, it is challenging to obtain an accurate system model due to unavoidable noise and disturbances. To address this issue, this paper proposes a fault diagnosis method for the maglev train levitation system. This method combines model‐based fault diagnosis with a data‐based implementation approach to maximize the benefits of both approaches while considering system noise and model uncertainty. Additionally, a secondary confirmation of fault isolation results is proposed to improve the reliability of fault diagnosis and minimize the effect of false alarms. Finally, a subsequent processing of fault data is recommended to enrich fault analysis methods.

A Power Grid Fault Diagnosis Method Considering Online Dynamic Alarm Information

With the continuous development of the smart grid and the gradual improvement of the demand for grid reliability and power grid self-healing capability, a large number of artificial intelligence technologies have been applied to the power grid. In order to improve the automation of the power grid, realize automatic fault identification and diagnosis after a power grid failure, and improve the efficiency of fault handling by the dispatching department, it is of great practical significance to use alarm information to achieve accurate fault judgment. However, the difference between the dynamic reading of alarm information flow and static samples in engineering applications is often ignored in the research of fault diagnosis using alarm information, which makes it difficult to apply fault diagnosis methods in engineering practice. Based on the characteristics of the LSTM algorithm, such as deep knowledge extraction ability, data mining ability and time series knowledge processing ability, this paper studies the power grid fault diagnosis method based on LSTM to adapt to the data of each sampling time window in the process of online fault diagnosis, solves the problems faced by the application of traditional diagnosis methods in engineering practice, and effectively improves the reliability of fault diagnosis.

Fault Diagnosis for China Space Station Circulating Pumps: Prototypical Network with Uncertainty Theory

Methods for fault diagnosis based on metric learning, in which a query sample is classified by picking the closest prototype from the support set based on their feature similarities, have been the subject of many studies. In real-world applications of in-orbit products, such as circulating pumps, the computation of similarity between different pairs is prone to different degrees of inaccuracy, especially epistemic uncertainty. Knowing and considering the uncertainty of similarity may improve fault detection accuracy. This article provides a unique approach to fault diagnosis based on Prototypical Network (Pro-Net) and Uncertainty Theory. In particular, we use epistemic uncertainty by altering the representation of prototypes from a deterministic scalar to an uncertain representation. To assess the similarity between a query and the prototypes in a support set, we calculate the uncertain distance between the pairs using cross-entropy. Experiments with symmetrical structures reveal that our proposed method significantly enhances classification precision and achieves state-of-the-art performance. It improves the reliability of fault diagnosis and reduces the risk of making erroneous judgments in safety-critical systems, decreasing the possibility of adverse consequences.

Fault diagnosis of oil-immersed transformer based on MGTO-BSCN

In order to improve the accuracy and reliability of fault diagnosis of oil-immersed power transformers, a fault diagnosis method based on the Modified Artificial Gorilla Troops Optimizer (MGTO) and the Stochastic Configuration Networks with Block Increments (BSCN) is proposed. First, the original artificial gorilla troop optimization algorithm is improved, which effectively improves the convergence speed and optimization accuracy of the algorithm. Secondly, the conventional Stochastic Configuration Networks (SCN) learning methodology is modified when the fault diagnosis model is constructed. The original SCN adopts point incremental approach to gradually add hidden nodes, while BSCN adopts block increment approach to learn features. It significantly accelerates training. MGTO algorithm is used to jointly optimize regularization parameter and scale factor in BSCN model, and the fault diagnosis model with the highest accuracy is constructed. The experimental results show that the accuracy of MGTO-BSCN for transformer fault diagnosis reaches 95.9%, which is 3.5%, 9.9% and 11.7% higher than BSCN fault diagnosis models optimized by GTO, Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) respectively, reflecting the superiority of MGTO algorithm. Meanwhile, the comparison with the traditional model shows that the proposed method has obvious advantages in diagnostic effect.

A multi-learner neural network approach to wind turbine fault diagnosis with imbalanced data

The data imbalance problem extensively exists in wind turbine fault diagnosis, resulting in the compromise between learning attention to majority and minority classes. In this paper, a deep neural network method is proposed to resolve the mentioned problem. Specifically, convolutional and recurrent neural networks are designed to extract spatial and temporal features within supervisory control and data acquisition (SCADA) measurements. To improve the reliability of fault diagnosis results by collective decision, a coarse learner and multiple fine learners are established. With the consideration of data imbalance and learning diversity, fault-related information can be revealed. Moreover, a learner selection scheme is designed to ensure high computational efficiency. The effectiveness of the proposed method is demonstrated by experiments based on simulated data and real-world SCADA measurements from a wind farm. Experimental results show that the accuracy in identifying health conditions can be improved by the proposed method regardless of the data imbalance. On the two datasets, the proposed method outperforms four benchmark approaches as the learning attention to all classes can be enhanced. Therefore, the proposed method is a promising solution to wind turbine fault diagnosis.

Multi-fault diagnosis for series-connected lithium-ion battery pack with reconstruction-based contribution based on parallel PCA-KPCA

Various faults of the lithium-ion battery threaten the safety and performance of the battery system. The early faults are difficult to detect and isolate owing to unobvious abnormality and the nonlinear time-varying characteristics of the battery. Herein, a multi-fault diagnosis strategy is proposed that focuses on detecting and isolating different types of faults, and estimating fault waveforms of the battery, including inconsistency evaluation, virtual connection fault, and external short circuit. First, the principal component analysis (PCA) model of the battery is established and the contribution is employed to detect the abnormity in the battery pack. Once the fault is detected, the parallel kernel principal component analysis (KPCA) technology is adopted to reconstruct the fault waveform of the battery parameters, including ohmic resistance, terminal voltage, and open-circuit voltage. These parameters are jointly taken as fault indexes improving the reliability of fault diagnosis. Finally, the proposed method is verified using amounts of tested data of eight cells in series. The results indicate that the contribution-based PCA method can accurately detect the fault. Furthermore, the reconstruction-based parallel PCA-KPCA can accurately estimate the fault waveform of the faulty battery, which helps investigate the fault degree and causes.

Fault Diagnosis of Aeroengine Control System Sensor Based on Optimized and Fused Multidomain Feature

Improving the efficacy and dependability of aeroengines requires timely and effective sensor fault diagnosis. Deep learning-based fault diagnosis method is a current research hotspot. To overcome some of the method’s existing shortcomings and improve the reliability of fault diagnosis, this paper proposes a novel intelligent fault diagnosis framework with higher quality features and more effective fault classifiers. The proposed plan includes three stages. Firstly, multidomain features (time and frequency domain features) are extracted to describe the sensor’s health from several dimensions. Secondly, the advanced Henry gas solubility optimization algorithm (HGSO) is applied to improve classification accuracy through feature selection, and the operating conditions and the features extracted by the network are fused as fault indicators. Finally, an adaptive deep belief network (ADBN) with relu-softsign combination activation layers, variable learning rate, and optimized network structure is proposed as the fault identifier. The advantages of the first two stages lie in the complete utilization of information and reducing the data dimension. In addition, the detection performance and the convergence speed is enhanced by the proposed ADBN. The experimental data are derived from a combination of measured and simulated data generated from the aeroengine model. The experimental results indicate that the improved method can produce better performance and outcomes than the unimproved methods for all fault scenarios, with a higher diagnostic accuracy of 98.1% and a reduced time of 98 s. The efforts of this study provide a efficient and adaptable way to aeroengine sensor fault diagnosis.

Fault Detection and Diagnosis in Air Handling Units with a Novel Integrated Decision Tree Algorithm

In air handling units (AHUs), wide attention has been attracted by data-driven fault detection and diagnosis techniques as the need for high-level expert knowledge of the concerned system is eliminated. In AHUs, decision tree induction is performed by means of classification and regression tree algorithm which is a data-driven diagnostic strategy based on decision tree. Expert knowledge as well as testing data may be used for validation of fault diagnosis reliability with easy interpretation and understanding ability offered by the decision tree. The diagnostic strategy established and its interpretability are increased by incorporating a regression model and steady-state detector with the model. ASHRAE, Oak Ridge National Lab (ORNL), National Renewable Energy Lab (NREL), Pacific Northwest National Lab (PNNL) and Lawrence Berkeley National Lab (LBNL) datasets are used for validation of the proposed strategy. High average F-measure and improved diagnostic performance may be achieved with this strategy. There is a compliance between the expert knowledge and certain diagnostic rules generated in the decision tree as seen from the expert knowledge implemented diagnostic decision tree interpretation. Based on the interpretation, it is evident that certain diagnostic rules are valid only under specific operating conditions and some of the generated diagnostic rules are not reliable. Data driven models are used for emphasizing the significance of interpretability of fault diagnostic models.

A Multi-Sensor Data Fusion Fault Diagnosis Method for Rail Vehicle Transmission System

The transmission system is a critical part of the rail vehicle bogie, mainly responsible for power drive and transmission. The performance of the transmission system directly affects the safe operation of trains. This paper proposes a multi-sensor data fusion method combined with deep belief nets (DBN) and fuzzy integral algorithm (FI) to improve the accuracy and reliability of fault diagnosis for rail vehicle transmission systems. First, multi DBN classifiers are established for the vibration signals collected from the key components of transmission systems to carry out preliminary fault diagnosis. Then, FI is applied to fuse the preliminary diagnosis results of DBNs to obtain comprehensive judgment. The effectiveness of the approach is verified by the bearing dataset collected in lab. This method is simple and practicable, and possesses certain applicability.

Multidomain Feature Fusion for Varying Speed Bearing Diagnosis Using Broad Learning System

Bearing is one of the most critical mechanical components in rotating machinery. To identify the running status of bearing effectively, a variety of possible fault vibration signals are recorded under multiple speeds. However, the acquired vibration signals have different characteristics under different speeds and environment interference, which may lead to different diagnosis results. In order to improve the fault diagnosis reliability, a multidomain feature fusion for varying speed bearing diagnosis using broad learning system is proposed. First, a multidomain feature fusion is adopted to realize the unified form of vibration characteristics at different speeds. Time‐domain and frequency‐domain features are extracted from the different speeds vibration signals. Then, the broad learning system is employed with the fused features for classification. Our experimental results suggest that, compared with other machine learning models, the proposed broad learning system model, which makes full use of the fused feature, can improve the diagnosis performance significantly in terms of both accuracy and robustness analysis.