Fault Diagnosis Technology Research Articles

Data-driven model for marine engine fault diagnosis using in-cylinder pressure signals

Effective diagnosis of marine engines that is crucial for safe and reliable ship operations requires fidel tools for the identification of critical faults. However, the unavailability of extensive measured data-sets corresponding to engine faulty conditions renders the development of such tools challenging. This study aims to develop a data-driven fault estimation model considering information extraction methods and regression techniques of low computational effort, namely multiple linear and polynomial regression. The required data-sets for healthy and faulty conditions for four critical faults and their combinations are generated by employing a calibrated zero-dimensional thermodynamic model representing a marine four stroke medium speed diesel engine, which is validated against engine shop trial measurements. Fourier analysis of the derived in-cylinder pressure profiles is employed to calculate the coefficients of the harmonic orders. Several harmonics coefficients sets are used as input to the regression models to estimate the severity of the four considered faults. The results demonstrate that initial 20 harmonics are sufficient to effectively estimate the severity for each fault, whereas polynomial regression is highly effective, exhibiting R 2 values greater than 98 % . This study provides insights on the data-driven simultaneous faults severity estimation, and as such it impacts the advancement of cost-effective diagnostic methods for marine engines.

Transformer fault diagnosis technology based on AdaBoost enhanced transferred convolutional neural network

Transformer fault diagnosis is an important means to ensure the stable operation of power systems, and Dissolved Gas Analysis (DGA) is a widely used method. However, existing intelligent diagnostic methods have problems such as insufficient expression of gas features, poor generalization of classification models, and lack of specialized deep optimization. On this basis, the authors proposed a transformer fault diagnosis technology based on AdaBoost enhanced transferred convolutional neural network, and constructed a transformer fault diagnosis scheme of “feature engineering, classification models, specialized deep optimization”. Firstly, the original five-dimensional gas features were expanded into new twenty-one-dimensional gas features to address the problem of insufficient expression of gas features. Factor analysis method was used to extract key information from twenty-one-dimensional gas features, and twelve-dimensional common factors were transformed to create the high-quality DGA data set. Secondly, the ensemble learning idea was referenced to construct ensemble classification models, and the best performing AdaBoost was obtained to solve the problem of poor generalization of single classification models. Finally, the transfer learning idea was referenced, the convolutional neural network was used as the basic classification model for AdaBoost, learning parameters were passed, and AdaBoost-TCNN with superior and stable classification effect was constructed to solve the problem of lack of specialized deep optimization. Test results on both private and public data sets show that, ensemble classification models achieved higher classification accuracy than single classification models, with significant advantages in Precision, Recall, and F1-score compared to single classification models. Among them, AdaBoost-TCNN achieved the highest classification accuracy of 97.33% and 95.35%, respectively, and obtained the most stable and significant Precision, Recall, and F1-score, all of which were superior to the ensemble classification models in the latest research. In addition, AdaBoost-TCNN also demonstrated a focus on key fault types. Therefore, the necessity of conducting this study, as well as the feasibility, practicality, and superiority of the technology proposed in this paper, have been fully demonstrated.

GDTE-based crack diagnosis for planetary gear: Mechanism, validation, and advantages compared to vibration-based technology

Effective health monitoring and fault diagnosis technologies are crucial to timely grasping the operation status of the planetary gear train (PGT). In this study, the mechanism for global dynamic transmission error (GDTE)-based crack diagnosis of PGT is revealed from dynamic responses and validated through experiments, and the advantages of this new technique are compared with the commonly used vibration-based technology. Initially, a dynamic model of the PGT considering various crack degrees and transfer path effects is established, and the effectiveness of the model response is verified through experiment. Subsequently, GDTE and vibration signals under different crack conditions are statistically analyzed in time domain and frequency domain to evaluate the differences in the ability to reveal gear failure information. Ultimately, the reasons for the different distribution of fault characterization information in the two signals are explained from a dynamic perspective. The results show that GDTE-based fault diagnosis exhibits greater sensitivity to gear crack degree compared to vibration-based monitoring technology, with both time and frequency domain responses containing richer fault characteristic information, particularly in low-frequency regions. The fault characteristics concentrated in higher-frequency regions in the vibration signal are mainly caused by the modulation between gear crack shock and the gear single and double tooth alternating meshing shock. Conversely, fault frequencies in GDTE signals are mainly found in low-frequency regions, as fault shocks in GDTE signals directly correlate with the rotational frequency of the cracked gear. Modulation effects and attenuation on the transfer path are identified as the main reasons for the weaker representation of gear fault information in vibration signals compared to GDTE signals.

Fault Diagnosis of Lithium-Ion Batteries Based on the Historical Trajectory of Remaining Discharge Capacity

In recent years, the number of safety accidents in new-energy electric vehicles due to lithium-ion battery failures has been increasing, and the lithium-ion battery fault diagnosis technology is particularly important to ensure the safe operation of electric vehicles. This paper proposes a method for lithium-ion battery fault diagnosis based on the historical trajectory of lithium-ion battery remaining discharge capacity in medium and long time scales. The method first utilizes the sparrow search algorithm (SSA) to identify the parameters of the second-order equivalent circuit model of the lithium-ion battery, and then estimates the state of charge (SOC) of the lithium-ion battery using the extended Kalman filter (EKF). The remaining discharge capacity is estimated according to the SOC, and finally the feature vectors are used to diagnose the faults using box plots on the medium and long time scales. Experimental results verify that the root mean squared error (RSME) and mean absolute error (MAE) of the proposed SOC estimation method are 0.0049 and 0.0034, respectively. This method can accurately identify the faulty single cell in a battery pack with low-capacity single cells and promptly detect any abnormalities in the single cell when a micro-short circuit fault occurs.

Audio-Based Engine Fault Diagnosis with Wavelet, Markov Blanket, ROCKET, and Optimized Machine Learning Classifiers.

Engine fault diagnosis is a critical task in automotive aftermarket management. Developing appropriate fault-labeled datasets can be challenging due to nonlinearity variations and divergence in feature distribution among different engine kinds or operating scenarios. To solve this task, this study experimentally measures audio emission signals from compression ignition engines in different vehicles, simulating injector failures, intake hose failures, and absence of failures. Based on these faults, a hybrid approach is applied to classify different conditions that help the planning and decision-making of the automobile industry. The proposed hybrid approach combines the wavelet packet transform (WPT), Markov blanket feature selection, random convolutional kernel transform (ROCKET), tree-structured Parzen estimator (TPE) for hyperparameters tuning, and ten machine learning (ML) classifiers, such as ridge regression, quadratic discriminant analysis (QDA), naive Bayes, k-nearest neighbors (k-NN), support vector machine (SVM), multilayer perceptron (MLP), random forest (RF), extra trees (ET), gradient boosting machine (GBM), and LightGBM. The audio data are broken down into sub-time series with various frequencies and resolutions using the WPT. These data are subsequently utilized as input for obtaining an informative feature subset using a Markov blanket-based selection method. This feature subset is then fed into the ROCKET method, which is paired with ML classifiers, and tuned using Optuna using the TPE approach. The generalization performance applying the proposed hybrid approach outperforms other standard ML classifiers.

Research on Digital Technology of Mechanical Structure Fault Diagnosis of Power Transformer Based on Comprehensive Feature Extraction and SABO-PNN

Research on Digital Technology of Mechanical Structure Fault Diagnosis of Power Transformer Based on Comprehensive Feature Extraction and SABO-PNN

Fault Detection Technology for Ultrapure Water Plant using Virtual Sensors for Pressure Control Valve and Indicators in Return Line

Objectives:This study aims to propose a technology that utilizes virtual sensors to quickly and accurately diagnose faults in valves and measurement devices that may occur in the return line of an ultrapure water plant.Methods:The empirical equations were derived, and the error range was calculated using operational data collected from three pieces of equipment (pressure control valve, pressure transmitter, and flow transmitter) in the recovery system of the ultrapure water process. Based on this, a virtual sensor is established.Results and Discussion:It was confirmed that the utilization of 24-hour data collected from the operation of the ultrapure water plant was enough to establish virtual sensors and could also estimate monthly data with high accuracy. In Phase 1, the fault diagnosis technology was found to estimate values more accurately and had a meaningful margin of error compared to Phase 2.Conclusion:It was possible to develop a fault detection technology using the virtual sensor derived from the empirical equation. The technology showed high performance in detecting anomalies in the flow meter and pressure control valve opening but had limitations in estimating consistently pressure values. Additionally, considering that there was no issue in estimating monthly data using the equations derived using 24 hours operational data. The fault detection algorithm could be used continuously if the empirical equations are updated by collecting a day's data when operating conditions change.

Fault Diagnosis Technology for Complex Mechanical Systems Based on Deep Learning

Traditional fault diagnosis methods are usually based on expert knowledge and experience, but this method often relies on manual analysis and judgment, which is time-consuming and inaccurate. Deep learning has become a hot spot because of the development of optimization algorithms and computer hardware, and has achieved great success in many applications. In the field, neural network, as a popular tool, can establish a feature recognition system between perceptron and classifier through training. This system includes the representation process of input layer, the promotion process of hidden layer and output layer. While ensuring the safe, stable and reliable operation of equipment and personal safety, improving the production efficiency of enterprises and enhancing the international competitiveness and influence of the industry are the new development trends of the design, production and maintenance guarantee system of manufacturing industry worldwide. At the same time, comprehensive testing and evaluation records of the operating status of the transmission equipment of the rolling mill can provide reliable reference for the diagnosis of faults and major and medium repairs of this type of rolling mill in the future. In practical engineering, due to different working conditions and complex environments, the types of faults that occur are unpredictable, which may result in a lack of samples available for training fault diagnosis models. This article mainly discusses and analyzes in depth the fault diagnosis technology of complex mechanical systems based on deep learning algorithms.

Aircraft engine fault diagnosis based on fused convolutional Transformer

Aircraft engines will face corrosion and erosion problems when working in harsh gas path environments for a long time, and the fault parameter characteristics are not obvious. Therefore, accurate aircraft engine fault diagnosis methods are of great significance to ensure the safe operation of aircraft. In order to improve the prediction accuracy, an aircraft engine fault diagnosis method based on fused convolutional Transformer is proposed. The self-attention mechanism is used to extract useful features and suppress redundant information. The maximum pooling layer (MaxPool) is introduced into the Transformer model to further reduce the model memory consumption and parameter quantity, and alleviate the overfitting phenomenon. The simulation data set of turbofan engines based on GasTurb modeling is used for verification. The results show that compared with the Transformer network and other traditional deep learning models Back Propagation Neural Networks (BP network), Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN), the accuracy rates are improved by 6.552%、28.117%、13.189%and respectively 10.29%, which proves the effectiveness of the proposed method and can provide a certain reference for aircraft engine fault diagnosis.

Envelope spectrum neural network with adaptive domain weight harmonization for intelligent bearing fault diagnosis under cross-machine scenarios

Accurate bearing fault diagnosis technology is highly important for ensuring the safe operation of mechanical equipment. Fault diagnosis methods can be roughly divided into signal processing-based methods (SPM) and data-driven methods (DDMs), which rely on physical knowledge and data knowledge, respectively. However, SPM cannot adapt to large data samples and dynamic parameter variations. DDMs did not consider physical representation consistency. To address the above issues, an envelope spectrum neural network (ESNN) is proposed for cross-machine bearing fault diagnosis. First, a deep transfer convolution network (DTCN) is constructed as the basic diagnostic framework. Second, a knowledge-to-model conversion strategy (KMC) is built to create ESNN, which utilizes equivalent convolution functions to construct the first two layers of DTCN, guiding the model to learn physical knowledge. Subsequently, an adaptive domain weight harmonization (ADWH) mechanism is proposed that can dynamically combine marginal distributions and joint distributions and automatically learn hidden features contained in physical and data knowledge, thus alleviating domain shift issues. Experimental evaluations are conducted using fault datasets from three different machines. The results show that, compared to models without knowledge guidance, ESNN achieves a 6.4% improvement in diagnostic accuracy. Compared to many advanced models, ESNN can achieve a maximum diagnostic accuracy improvement of 35.94%. The code of the ESNN can be found at https://github.com/John-520/ESNN.

Research on Fault Prediction and Diagnosis of Mechanical and Electrical Equipment in Construction Machinery based on Big Data Technology

This article aims to explore how to use big data technology for fault prediction and diagnosis of mechanical and electrical equipment in construction machinery. Through steps such as data collection and storage, data feature extraction and selection, and data analysis and processing, a fault prediction and diagnosis model is constructed to achieve real-time monitoring and early warning. The accuracy and efficiency of fault prediction are improved through decision feedback and algorithm optimization.

Actuator fault diagnosis and severity identification of turbofan engines for steady-state and dynamic conditions

Actuator faults can be critical in turbofan engines as they can lead to stall, surge, loss of thrust and failure of speed control. Thus, fault diagnosis of gas turbine actuators has attracted considerable attention, from both academia and industry. However, the extensive literature that exists on this topic does not address identifying the severity of actuator faults and focuses mainly on actuator fault detection and isolation. In addition, previous studies of actuator fault identification have not dealt with multiple concurrent faults in real time, especially when these are accompanied by sudden failures under dynamic conditions. This study develops component-level models for fault identification in four typical actuators used in high-bypass ratio turbofan engines under both dynamic and steady-state conditions and these are then integrated with the engine performance model developed by the authors. The research results reported here present a novel method of quantifying actuator faults using dynamic effect compensation. The maximum error for each actuator is less than 0.06% and 0.07%, with average computational time of less than 0.0058 s and 0.0086 s for steady-state and transient cases, respectively. These results confirm that the proposed method can accurately and efficiently identify concurrent actuator fault for an engine operating under either transient or steady-state conditions, even in the case of a sudden malfunction. The research results demonstrate the potential benefit to emergency response capabilities by introducing this method of monitoring the health of aero engines.

A Distributed Cyber-Physical Framework for Sensor Fault Diagnosis of Marine Internal Combustion Engines

A Distributed Cyber-Physical Framework for Sensor Fault Diagnosis of Marine Internal Combustion Engines

Review of imbalanced fault diagnosis technology based on generative adversarial networks

Abstract In the field of industrial production, machine failures not only negatively affect productivity and product quality, but also lead to safety accidents, so it is crucial to accurately diagnose machine failures in time and take appropriate measures. However, machines cannot operate with faults for extended periods, and the diversity of fault modes results in limited data collection, posing challenges to building accurate fault prediction models. Despite recent advancements, intelligent fault diagnosis methods based on traditional sampling and machine learning have shown notable progress. Nonetheless, these methods heavily rely on human expertise, making it challenging to extract comprehensive feature information. To address these challenges, numerous imbalance fault diagnosis methods based on generative adversarial networks (GANs) have emerged, GANs can generate realistic samples that conform to the distribution of the original data, showing promising results in diagnosing imbalances in critical components such as bearings and gears, despite their great potential, GAN methods also face challenges, including difficulties in training and generating abnormal samples. However, whether it is GAN-based resampling technology or traditional sampling technology, there are fewer reviews on noise-containing imbalance, intra- and inter-class dual imbalance, multi-class imbalance, time series imbalance and other problems in small samples, and there is a lack of a more comprehensive summary of the solutions to the above imbalance problems. Therefore, the purpose of this paper is to deeply explore the imbalance problems under various failure modes, and review and analyze the research methods and results based on GANs on this basis. By suggesting future research directions, this paper aims to provide guidance and reference for research in the field of industrial production maintenance.

A digital twin system for centrifugal pump fault diagnosis driven by transfer learning based on graph convolutional neural networks

In industrial sectors such as shipping, chemical processing, and energy production, centrifugal pumps often experience failures due to harsh operational environments, making it challenging to accurately identify fault types. Traditional fault diagnosis methods, which heavily rely on existing fault datasets, suffer from limited generalization capabilities, especially when substantial labeled and specific fault sample data are lacking. This paper proposes a novel fault diagnosis approach for centrifugal pumps, utilizing a digital twin (DT) framework powered by a graph transfer learning model to address this issue. Firstly, a high-fidelity DT model is constructed to simulate the flow-induced vibration response of the impeller under different health states to enrich the type and scale of the dataset. Secondly, a graph convolutional neural networks (GCN) model is constructed to learn the knowledge of simulation data, and the Wasserstein distance between simulation data and measured data is optimized for adversarial domain adaptation, thereby achieving efficient cross-domain fault diagnosis. Experimental results demonstrate that the proposed algorithm delivers effective fault diagnosis with minimal prior knowledge and outperforms comparable models. Furthermore, the DT system developed using the proposed model enhances the operational reliability of centrifugal pumps, reduces maintenance costs, and presents an innovative application of DT technology in industrial fault diagnosis.

Fault diagnosis technology overview for lithium‐ion battery energy storage station

AbstractWith an increasing number of lithium‐ion battery (LIB) energy storage station being built globally, safety accidents occur frequently. Diagnosing faults accurately and quickly can effectively avoid safe accidents. However, few studies have provided a detailed summary of lithium‐ion battery energy storage station fault diagnosis methods. In this paper, an overview of topologies, protection equipment, data acquisition and data transmission systems is firstly presented, which is related to the safety of the LIB energy storage power station. Then, existing fault diagnosis technologies are reviewed in detail. Finally, the future developing trends of fault diagnosis technology are discussed.

A comprehensive review of BMS fault analysis in pure electric vehicles

Pure electric vehicle technology faces numerous technical challenges during the process of independent research and development, with the safety and endurance of power batteries being particularly critical. The Battery Management System (BMS) is a core factor affecting the performance and safety of electric vehicles. The normal operation of the BMS is essential for ensuring the reliability of electric vehicles. However, in practical applications, the BMS may encounter faults that can lead to a decline in battery performance, a reduction in lifespan, and even trigger safety incidents. Therefore, the development of an efficient and accurate fault analysis and diagnostic system is particularly important. This paper systematically explores the fault issues of electric vehicle BMS, including the composition, working principles, and main functions of the BMS, as well as the classification, level definition, case analysis, and resolution measures of faults. The article also provides a detailed introduction to fault analysis methods, including qualitative and quantitative analyses. Finally, based on recent hot topics and the latest research progress, it is concluded that model-based fault diagnosis will become increasingly important. This paper aims to provide a reference for the development of the field of electric vehicle battery management fault diagnosis technology.

Fault Diagnosis of Marine Diesel Engine Based on Multi-scale Time Domain Decomposition and Convolutional Neural Network

Abstract Marine diesel engines work in an environment with multiple excitation sources. Effective feature extraction and fault diagnosis of diesel engine vibration signals have become a hot research topic. Time-domain synchronous averaging (TSA) can effectively handle vibration signals. However, the key phase signal required for TSA is difficult to obtain. During signal processing, it can result in the loss of information on fault features. In addition, frequency multiplication signal waveforms are mixed. To address this problem, a multi-scale time-domain averaging decomposition (MTAD) method is proposed and combined with signal-to-image conversion and a convolutional neural network (CNN), to perform fault diagnosis on a marine diesel engine. Firstly, the vibration signals are decomposed by MTAD. The MTAD method does not require the acquisition of the key phase signal and can effectively overcome signal aliasing. Secondly, the decomposed signal components are converted into 2-D images by signal-to-image conversion. Finally, the 2-D images are input into the CNN for adaptive feature extraction and fault diagnosis. Through experiments, it is verified that the proposed method has certain noise immunity and superiority in marine diesel engine fault diagnosis.

Fault Diagnosis Review of Proton Exchange Membrane Fuel Cell Systems: Fault Mechanisms, Detection and Identification, and Fault Mitigation

Proton exchange membrane fuel cell (PEMFC) has become a hotspot due to its high efficiency, compact structure, and good dynamic operation efficiency. However, problems such as poor reliability and short lifespan create bottlenecks in its large‐scale applications. There has been a large amount of research on fault diagnosis and health management techniques dedicated to addressing the lifespan issues of PEMFC systems. This article provides an in‐depth analysis on the fault mechanism of PEMFC and systematically sorts out the types, causes, and impacts of faults. On this basis, the research progress of PEMFC fault diagnosis technology is summarized, and the measurement characterization methods for fuel cell status monitoring and fault detection are summarized. The literatures of model‐based and data‐driven fault identification methods are summarized and compared. The relevant mitigation measures for PEMFC faults are discussed. Finally, based on the challenges in the current research of fault diagnosis, people mainly conduct research on fault model, online diagnostic technology, and improving diagnostic mechanisms. Overall, this article can provide useful summary and guidance for future research.

Research on Fault Diagnosis and Predictive Maintenance Technology for Construction Crane Equipment

Construction lifting machinery equipment plays an important role in construction operations, but due to complex environments and high-intensity use, it is prone to malfunctions, causing economic losses to enterprises and users. This research aims to realize intelligent monitoring, fault diagnosis and predictive maintenance of construction machinery equipment based on industrial Internet platform, Internet of Things and artificial intelligence technology, so as to reduce maintenance costs, improve equipment efficiency, and promote digital transformation of the industry.