Fault Diagnosis System Research Articles

Model based fault detection diagnosis and classification in shell and tube heat exchanger using intelligent techniques

A method for Model-Based Approach for Fault Detection and Diagnosis has been put forth in this research. The purpose of this study is to build a heat exchanger model and to locate, categorize faults in the heat exchanger system. One of the known methods for fault control in industrial processes is fault detection and diagnosis (FDD). Both linear and non-linear systems can benefit from the FDD technique; this work describes fault identification and diagnosis for the non-linear system, the heat exchanger. A realistic mathematical model of the heat exchanger with its set of inputs and outputs has therefore been constructed, as the fault detection of the heat exchanger is related to the mathematical model for its operation. Once the mathematical model has been realized using the formulated equation, the process passes on to fault detection. Residual, which defines the failure symptoms of the system, is used to detect faults. In order to prevent failures and defective circumstances from occurring in existing systems, observer design is employed as one of the most powerful and reliable approaches residual generation. Fault diagnosis is carried out using a Fuzzy logic controller (FLC), which is designed based on the residuals obtained, and fault classification is made using the FLC via a fixed threshold. The paper's primary goals are to reduce errors and enhance non-linear systems' overall performance in industrial processes. According to the findings of the simulation, errors in sensors, processes, and actuators can be classified according to a set threshold, and they can be controlled using a traditional PID controller.

Optimal Design of Data Acquisition System for Fault Diagnosis of Hydraulic Machinery

Abstract Fault diagnosis of hydraulic machinery has high requirements on the synchronization of monitoring data and the anti-interference of monitoring equipment. The performance and data quality of the existing operation monitoring equipment of the unit cannot meet the requirements. At the same time, due to the variety of sensors and the different wiring methods of different types of sensors, the preparation work such as wiring of data acquisition equipment in the test is complicated. This paper analyzes the application requirements of fault diagnosis data and the problems existing in the conventional data collection process. A data collection solution specified for diagnosis of hydraulic machinery, which better solves the problem of data synchronization, is designed. The new designed system has high sampling accuracy and anti-interference characteristics, as well as the advantages of strong capability and simple equipment deployment. The proposed system has been applied in multiple power stations, pumping stations and pump test platforms. The relevant functions have been effectively verified.

DNOCNet: A Novel End-to-End Network for Induction Motor Drive Systems Fault Diagnosis Under Speed Fluctuation Condition

DNOCNet: A Novel End-to-End Network for Induction Motor Drive Systems Fault Diagnosis Under Speed Fluctuation Condition

Application of Digital Galvanometer Scanner System for CO2 Fractional Laser Safety Improvement

An analog PID controller-based galvanometer scanner is widely used by fractional laser medical equipment (FLME) to scan lasers across tissue surfaces, achieving the desired therapeutic effect. This type of driver, primarily composed of passive components and operational amplifiers, can only accept commands from the central controller of the FLME, with a simple hardware circuit-based fault diagnosis; thus, the safety of the FLME is compromised. To address these issues, the failure mechanisms of galvanometers and their impact on the safety of FLME are thoroughly analyzed first. Then, an adaptive limit protection method, a coil open circuit fault diagnosis, a communication timeout protection based on two handshakes, and a galvanometer control timeout protection are proposed, respectively, based on a digital driver platform, to supplement the deficiencies in the original fault diagnosis and protection system. This ensures the safety of the FLME. Finally, the effectiveness of the proposed strategies is validated through experiments.

Neural network-based fault diagnosis and fault-tolerant control for nonlinear systems with output measurement noise

Neural network-based fault diagnosis and fault-tolerant control for nonlinear systems with output measurement noise

Radiometric Infrared Thermography of Solar Photovoltaic Systems: An Explainable Predictive Maintenance Approach for Remote Aerial Diagnostic Monitoring

Solar photovoltaic (SPV) arrays are crucial components of clean and sustainable energy infrastructure. However, SPV panels are susceptible to thermal degradation defects that can impact their performance, thereby necessitating timely and accurate fault detection to maintain optimal energy generation. The considered case study focuses on an intelligent fault detection and diagnosis (IFDD) system for the analysis of radiometric infrared thermography (IRT) of SPV arrays in a predictive maintenance setting, enabling remote inspection and diagnostic monitoring of the SPV power plant sites. The proposed IFDD system employs a custom-developed deep learning approach which relies on convolutional neural networks for effective multiclass classification of defect types. The diagnosis of SPV panels is a challenging task for issues such as IRT data scarcity, defect-patterns’ complexity, and low thermal image acquisition quality due to noise and calibration issues. Hence, this research carefully prepares a customized high-quality but severely imbalanced six-class thermographic radiometric dataset of SPV panels. With respect to previous approaches, numerical temperature values in floating-point are used to train and validate the predictive models. The trained models display high accuracy for efficient thermal anomaly diagnosis. Finally, to create a trust in the IFDD system, the process underlying the classification model is investigated with perceptive explainability, for portraying the most discriminant image features, and mathematical-structure-based interpretability, to achieve multiclass feature clustering.

Privacy-preserving intelligent fault diagnostics for wind turbine clusters using federated stacked capsule autoencoder

The emergence of Internet of Things (IoT) technologies in the field of health monitoring has introduced the paradigm of Industrial Internet of Things (IIoT) to the industry. IIoT systems provide enterprises with a substantial volume of monitoring data for industrial equipment health monitoring, facilitating the development of artificial intelligence fault diagnosis models. However, a singular industrial entity often encounters limitations in collecting sufficient training data in practical scenarios. Moreover, the sharing of confidential information among entities is strictly prohibited due to concerns regarding intellectual property and data security. This study proposes a fault diagnosis system that addresses this issue by incorporating a capsule-based fault feature expression into the federated learning (FL) framework. The system comprises clients distributed across multiple factories and a central server hosted in the cloud. The client models are trained on local private datasets, and then knowledge fusion is achieved by uploading intrinsic templates and pose matrices to the central server. The proposed method offers the advantage of reducing transmission burden and enhancing data security in comparison to existing FL approaches. Besides, a capsule knowledge alignment algorithm is proposed to update the capsule-based fault feature expression ona central server. To simulate real fault diagnosis application scenarios, two similar fault simulation platforms are built to acquire isolated fault diagnosis datasets. The effectiveness of the proposed method is verified using these datasets.

Improved fault detection and diagnosis using graph auto encoder and attention-based graph convolution networks

A powerful fault detection and diagnosis (FDD) system plays a pivotal role in achieving operational excellence by maximizing system performance, optimizing maintenance strategies, and ensuring the longevity and resilience of process plants. In the context of FDD for multivariate sensor data, this study presents an improved FDD approach using graph-based neural networks. This graph neural network uses an adjacency matrix developed by extracting the expert domain knowledge and topological information of the multi-sensor system. This additional graph representation of the system is incorporated along with multivariate sensor data to capture the spatial and temporal information in neural networks efficiently. In this regard, we propose and evaluate: 1) A Graph Auto Encoder (GAE) based fault detection strategy and 2) An Attention-based Spatial Temporal Graph Convolution Network (ASTGCN) based fault diagnosis methodology. By leveraging the additional knowledge in the form of graphs, the GAE captures the complex relationships and dependencies among sensors, enabling effective anomaly detection, which identifies abnormal patterns and deviations from normal behavior, thus indicating potential faults in the system. The ASTGCN incorporates attention mechanisms to selectively focus on relevant sensor nodes and capture their spatial and temporal dependencies for fault diagnosis. The effectiveness of the proposed FDD approach is demonstrated using the benchmark Tennessee Eastman Process (TEP) problem. The results show that the proposed approaches outperform traditional methods and highlight the importance of leveraging graph-based knowledge for FDD in complex systems.

Fault detection and diagnosis in light commercial buildings’ HVAC systems: A comprehensive framework, application, and performance evaluation

The data-driven approach currently dominates the field of Automatic Fault Detection and Diagnosis (AFDD) in HVAC systems. However, a significant concern lies in the prevalent use of labeled experimental and simulation data, which often does not represent real-world operational conditions. This study unveils a comprehensive framework for AFDD in light commercial buildings, effectively leveraging unlabeled raw data extracted directly from their Building BAS. Its main goal is to provide a versatile methodology tailored for real-world applicability. Buildings classified as “light commercial” typically have less than 2,500 square meters of floor area and no more than six stories, such as small offices, medical facilities, banks, small manufacturing facilities, etc. A common feature of these buildings is the fact that the HVAC systems tend to be relatively simple and have similar configurations, thus making it easy to develop scalable and reproducible fault detection methods. This paper focuses on a practical case study in a light commercial building HVAC system situated in Montreal, Canada, encompassing a single Air Handling Unit (AHU) and four Variable Air Volume (VAV) reheating boxes to evaluate the framework. This comprehensive framework encompasses a sequence of sub-objectives: creating a sizable, synchronized raw dataset from diverse BAS sensor tags, comprehensive data cleansing to address inconsistencies, developing an anomaly detection method, investigating these anomalies to extract underlying rules, and finally, dataset labeling. An AFDD classification model is then applied to evaluate its ability to distinguish normal from faulty conditions across various fault types. The study highlights the potential of dimensional reduction techniques and unsupervised clustering for effective anomaly detection in light commercial buildings, as well as the power of the Decision Tree classifier for uncovering hidden patterns, especially in anomaly conditions. It also highlights the significance of addressing imbalanced datasets in AFDD and the complexities of detecting sizing-related faults. Despite these challenges, the framework exhibits robust performance in detecting and diagnosing a range of HVAC faults. It offers a systematic and adaptable approach for handling real-world operational data in light commercial building HVAC systems, extendible to other building types, bridging the gap between data-driven methods and practical applications.

Enhancing Fault Diagnosis in Industrial Processes through Adversarial Task Augmented Sequential Meta-Learning

This study introduces the Adversarial Task Augmented Sequential Meta-Learning (ATASML) framework, designed to enhance fault diagnosis in industrial processes. ATASML integrates adversarial learning with sequential task learning to improve the model’s adaptability and robustness, facilitating precise fault identification under varied conditions. Key to ATASML’s approach is its novel use of adversarial examples and data-augmentation techniques, including noise injection and temporal warping, which extend the model’s exposure to diverse operational scenarios and fault manifestations. This enriched training environment significantly boosts the model’s ability to generalize from limited data, a critical advantage in industrial applications where anomaly patterns frequently vary. The framework’s performance was rigorously evaluated on two benchmark datasets: the Tennessee Eastman Process (TEP) and the Skoltech Anomaly Benchmark (SKAB), which are representative of complex industrial systems. The results indicate that ATASML outperforms conventional meta-learning models, particularly in scenarios characterized by few-shot learning requirements. Notably, ATASML demonstrated superior accuracy and F1 scores, validating its effectiveness in enhancing fault-diagnosis capabilities. Furthermore, ATASML’s strategic incorporation of task sequencing and adversarial tasks optimizes the training process, which not only refines learning outcomes but also improves computational efficiency. This study confirms the utility of the ATASML framework in significantly enhancing the accuracy and reliability of fault-diagnosis systems under diverse and challenging conditions prevalent in industrial processes.

WBUN: an interpretable convolutional neural network with wavelet basis unit embedded for fault diagnosis

Convolutional Neural Network (CNN) is extensively applied in mechanical system fault diagnosis. However, the absence of transparent decision mechanisms in CNNs hinders credibility. To address these challenges, this paper proposes an interpretable wavelet basis unit convolutional network (WBUN). This network incorporates meticulously designed wavelet basis unit (WBU) functions into convolutional layer, creating the interpretable wavelet basis unit convolutional (WBUConv) layer. Convolutional kernels with clear physical significance enable the WBUConv layer to extract fault-related features in both time and frequency domains, enhancing diagnostic performance, and interpreting the CNN’s attention frequency along with the convolutional kernel’s training outcomes. In this paper, three WBU functions are designed to construct the corresponding WBUNs, and their effectiveness and interpretability are verified through three sets of mechanical fault diagnosis experiments. Meanwhile, experimental results demonstrate the WBUConv layer’s remarkable advantages in noise robustness, convergence speed, and strong generalization ability.

Optimizing Accumulator Performance in Hydraulic Systems through Support Vector Regression and Rotational Factors

The piston-type accumulator is an energy storage device in hydraulic–pneumatic systems, playing a significant role in industries such as petrochemicals, heavy machinery, and steel metallurgy. The displacement parameters of the piston-type accumulator are vitally important for fault diagnosis and early warning in hydraulic systems. Traditional displacement measurement methods cannot meet the requirements of the internal testing environment of the accumulator. Therefore, this paper proposes an accumulator piston displacement signal compensation method based on rotational factors and support vector regression. Firstly, empirical mode decomposition is utilized to denoise the signal. Then, rotational factors are used to generate a delay compensation module to compensate for the signal attenuation and time delay caused by metallic reflection and scattering within the cylinder of the radar signal. The support vector regression model is improved based on a hash table to enhance its computational efficiency and achieve radar displacement signal compensation. Finally, a simulation experiment is designed to verify the effectiveness of the proposed method.

ESG guidance and artificial intelligence support for power systems analytics in the energy industry.

In order to increase the precision and effectiveness of power system analysis and fault diagnosis, this study aims to assess the power systems in the energy sector while utilizing artificial intelligence (AI) and environmental social governance (ESG). First, the ESG framework is presented in this study to fully account for the effects of the power system on the environment, society, and governance. Second, to coordinate the operation of various components and guarantee the balance and security of the power system, the CNN-BiLSTM power load demand forecasting model is built by merging convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). Lastly, the particle swarm optimization (PSO) algorithm is used to introduce and optimize the deep belief network (DBN), and a power grid fault diagnostic model is implemented using the PSO technique and DBN. The model's performance is assessed through experimentation. The outcomes demonstrate how the CNN-BiLSTM algorithm significantly increases forecasting accuracy while overcoming the drawback of just having one dimension of power load data. The values of 0.054, 0.076, and 0.102, respectively, are the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Effective processing of large-scale nonlinear data is achieved in the area of power grid fault diagnosis, resulting in prediction accuracy of 96.22% and prediction time of only 129.94s. This is clearly better than other algorithms and increases fault prediction efficiency and accuracy. Consequently, the model presented in this study not only produces impressive results in fault diagnosis and load demand forecasting, but also advances the field of power system analysis in the energy industry and offers a significant amount of support for the sustainable and intelligent growth of the energy industry.

Fault diagnosis method of PEMFC system based on ensemble learning

Due to the coupling of multiple physical fields inside proton exchange membrane fuel cell (PEMFC) and the complex and changeable external application scenarios, the faults of PEMFC system greatly affects its engineering application. The paper proposes an ensemble learning-based fault diagnosis method for PEMFC systems, utilizing five distinct algorithms to develop an integrated ensemble classifier for fault detection. The original sample dataset collected from fuel cells undergoes a process of deep feature extraction to obtain a composite feature sample dataset, which is then inputted into an ensemble classifier to establish a fault diagnosis model. The experimental data of four kinds of faults of 80 W fuel cell test bench, EC fuel cell system and 50 W water-cooled fuel cell test bench are analyzed by using this classification model. The results show that the accuracy and generalization of the ensemble learning algorithm for different fault diagnosis of PEMFC system in different application environments are verified.

Two-step vibration-based machine learning model for the fault detection and diagnosis in rotating machine and its blind application

A robust and reliable condition monitoring and fault diagnosis system is crucial for an efficient operation of industries. Because of the advances in technologies over the past few decades, there is an increased interest in developing intelligent systems to perform tasks that traditionally rely on knowledge, experience and expertise of an individual. It is known that unexpected breakdowns have wide implications in production processes. Thus, it is vital to be able to know the machine condition and detect at the earliest possible stage the defects when they occur. Aiming at an industrial application, in this study, a two-step approach is proposed for the fault detection and diagnosis of rotor-related faults. The implemented algorithm is a pattern recognition supervised artificial neural network, which through information extracted from vibration signals allows one to identify the health status of the machine. In the first step, the model identifies whether the machine is healthy or faulty. This is important information for any industry to operate the machines. Once the machine condition (healthy or faulty) is known and if it is faulty, then only faulty machine parameters are used in the second step to know the specific fault. The model is initially based on existing experimental data, and then, it is further validated with mathematically generated data. The proposed two-step approach model and the trained framework are applied blindly at a different machine speed, where the dynamics of machine is expected to be different. The excellent results obtained suggest this approach as a possibility for industrial application.

Wear diagnosis for rail profile data using a novel multidimensional scaling clustering method

AbstractThe diagnosis of railway system faults is significant for its comfort, efficiency, and safety. The rail surface wear is the key impact factor when considering the health conditions of rails. This paper accomplishes contactless rail wear diagnosis by using multidimensional scaling based on a novel informational dissimilarity measure (IDM) to cluster intact and different worn rail profile data. The IDM uses weighted‐probability distribution of dispersion patterns to extract accurate time domain features from rail profile data, and the loss of information is minimized, which can greatly improve the accuracy for wear diagnosis. All the analyzing data for real experiments are collected by a laser scanner camera on an inspection car, where heavy‐haul railway rails with different types of surface wear are inspected. Experimental results with simulated and reality‐based data show that the proposed methods can identify worn profile data and discriminate different types of worn profiles more effectively when compared with existing methods. Thus, the proposed method offers a new thinking for the diagnosis of rail surface wear for heavy‐haul railways.

Application of Bayesian networks based on Sequential Monte Carlo simulation and physical model in fault diagnosis of horizontal three-phase separator system

In offshore production platforms, horizontal three-phase separator is common process equipment. Its main function is to complete the dehydration and degassing of crude oil. Separator system is more complex, and its failure may cause significant economic losses and disastrous consequences. Therefore, it is critical to accurately and quickly identify where and why faults occur in the separator system. In this study, separator fault diagnosis model based on Bayesian networks is developed. Moreover, Sequential Monte Carlo simulation and physical model are introduced to overcome field problems such as missing separator failure data and the inability of experts to provide accurate empirical knowledge. Using this model, 13 faults in a separator in an offshore crude oil processing system are successfully diagnosed. Meanwhile, the proposed model is compared with deep neural network, convolutional neural network, and deep residual network, with accuracy rates of 100%, 91.34%, 87.99%, and 94.62%, respectively. Then, the diagnostic accuracy of each model for different faults is also compared in this paper under various signal-to-noise ratios. The results show that the method proposed in this paper has better noise immunity compared to the other three models. Therefore, the accuracy and robustness of the proposed model is further demonstrated. Finally, to analyze the fault-tolerance of the proposed model, 2–3 error evidence is randomly entered. The results show that the proposed model has better fault tolerance compared to data-driven Bayesian networks.

Performance evaluation of deep learning approaches for fault diagnosis of rotational mechanical systems using vibration, sound, and acoustic emission signals

The present study emphasizes an optimized deep learning algorithm for gearbox fault detection using vibration, sound, and acoustic emission signals. Statistical and acoustic features are extracted from these signals, and various neural network algorithms are explored. The supervised deep feed forward neural network (DFFNN) demonstrates excellent performance with vibration signals but limited accuracy with sound and acoustic emission signals. To address this, unsupervised algorithms are optimized and compared with vibration-based classification. The findings show that unsupervised neural networks, particularly the auto-encoder and stacked auto-encoder architectures, achieve improved classification accuracy by leveraging the unique characteristics of acoustic emission signals. The unsupervised models also effectively overcome the vanishing gradient problem via regularization, enhancing their training efficiency. The stacked auto-encoder, with multiple layers of encoders and decoders, reduces computation time by 40% and memory consumption. These optimized algorithms hold promise for automated fault detection systems. The auto-encoder and stacked auto-encoder, utilizing vibration, sound, and acoustic emission signals, offer enhanced classification accuracy and can facilitate real-time monitoring of rotating mechanical systems. However, further optimization is needed to maximize their performance. In a nutshell, the supervised DFFNN excels in utilizing vibration signals for fault detection, while the unsupervised models exploit the distinctive characteristics of acoustic emission signals. Future research will focus on refining these algorithms to enhance their effectiveness. Implementing these optimized deep learning approaches can lead to autonomous fault detection systems, eliminating the need for continuous human supervision.

Fault Identification in Optical Transport Network Using CNN

The growing diversity Regarding the provision of information and services transmitted Using optical technology transport networks (OTN) has made network survival a crucial issue in current study. Fault detection refers to the process of identifying faults that arise from various issues such as packet loss, disconnection, and others in the OTN. The determination of the fault location in an OTN is highly significant in the analysis of the resilience of optical networks. This study presents a fault diagnosis system that utilises a Convolutional Neural Network (CNN), focusing primarily on distinguishing between hard faults (HF) and soft faults (SF). The CNN is implemented in where the fault is situated domain of OTN to determine the presence or absence of potential fault locations. The notion An F-Measure is implemented in order to quantify the impact of positioning.utilising Location, time, mean squared error (MSE), and F-measure. The scientific investigation demonstrates that the suggested CNN neural network achieves the highest performance. The suggested CNN has a reduced localization time and achieves an F1-score of 0.98 after 85 iterations. This level of accuracy and real-time performance fulfils the requirements for fault identification. Hence, there is significant potential and practical utility in incorporating neural networks in the field of identifying and locating faults in optical transport networks.

Research on Fault Diagnosis Method of CNC Machine Tools Based on Integrated MPA Optimised Random Forests

INTRODUCTION: Intelligent diagnosis of CNC machine tool faults can not only early detection and troubleshooting to improve the reliability of machine tool operation and work efficiency, but also in advance of the station short maintenance to extend the life of the machine tool to ensure that the production line of normal production.OBJECTIVES: For the current research on CNC machine tool fault diagnosis, there are problems such as poorly considered feature selection and insufficiently precise methods.METHODS: This paper proposes a CNC machine tool fault diagnosis method based on improving random forest by intelligent optimisation algorithm with integrated learning as the framework. Firstly, the CNC machine tool fault diagnosis process is analysed to extract the CNC machine tool fault features and construct the time domain, frequency domain and time-frequency domain feature system; then, the random forest is improved by the marine predator optimization algorithm with integrated learning as the framework to construct the CNC machine tool fault diagnosis model; finally, the validity and superiority of the proposed method is verified by simulation experiment analysis.RESULTS: The results show that the proposed method meets the real-time requirements while improving the diagnosis accuracy.CONCLUSION: Solve the problem of poor accuracy of fault diagnosis of CNC machine tools and unsound feature system.