Fault Diagnostics Research Articles

Research on Power Equipment Fault Diagnosis Based on Improved SVM Algorithm

Recently, Data collected from safety relays and circuit breakers has been used in fault diagnostics to identify power system components that are either failing or have tripped. A tiny percentage of heating zones may cause problems when identifying faults, although the chance of aberrant heat production in power equipment is modest. The power transformer is one of the most crucial components of any electrical system. Discovering any concealed defects in power equipment is essential to ensuring a continuous supply of electrical power for customers. The challenging characteristic of such fault diagnosis in power equipment is the greater potential for harm and a great deal of time invested in testing and training. Hence, in this research, Smart Support Vector Machine enabled Artificial Neural Network (SVM-ANN) technologies have been improved for fault diagnosis in power equipment for industry. The Genetic Algorithm (GA) process the power equipment for industrial data processing and monitoring in that fault diagnosis. The classification of strategies used and their relationships to power equipment processes to identify significant trends and research problems related to intelligent fault diagnosis systems for industry. The experimental analysis of SVM-ANN outperforms fault diagnosis using the power equipment industry in terms of performance, accuracy, prediction ratio, efficiency, and error rate.

Smart Sensor-Based Monitoring Technology for Machinery Fault Detection.

Rotary machines commonly use rolling element bearings to support rotation of the shafts. Most machine performance imperfections are related to bearing defects. Thus, reliable bearing condition monitoring systems are critically needed in industries to provide early warning of bearing fault so as to prevent machine performance degradation and reduce maintenance costs. The objective of this paper is to develop a smart monitoring system for real-time bearing fault detection and diagnostics. Firstly, a smart sensor-based data acquisition (DAQ) system is developed for wireless vibration signal collection. Secondly, a modified variational mode decomposition (MVMD) technique is proposed for nonstationary signal analysis and bearing fault detection. The proposed MVMD technique has several processing steps: (1) the signal is decomposed into a series of intrinsic mode functions (IMFs); (2) a correlation kurtosis method is suggested to choose the most representative IMFs and construct the analytical signal; (3) envelope spectrum analysis is performed to identify the representative features and to predict bearing fault. The effectiveness of the developed smart sensor DAQ system and the proposed MVMD technique is examined by systematic experimental tests.

Implementing Industry 4.0: An In-Depth Case Study Integrating Digitalisation and Modelling for Decision Support System Applications

The scientific community has shown considerable interest in Industry 4.0 due to its capacity to revolutionise the manufacturing sector through digitalisation and data-driven decision-making. However, the actual implementation of Industry 4.0 within complex industrial settings presents obstacles that are typically beyond the scope of mainstream research articles. In this paper, a comprehensive case-study detailing our collaborative partnership with a leading medical device manufacturer is presented. The study traces its evolution from a state of limited digitalisation to the development of a digital intelligence platform that leverages data and machine learning models to enhance operations across a wide range of critical machines and assets. The main business objective was to enhance the energy efficiency of the manufacturing process, thereby improving its sustainability measures while also saving costs. The project encompasses energy modelling and analytics, Fault Detection and Diagnostics (FDD), renewable energy integration and advanced visualisation tools. Together, these components enable informed decision making in the context of energy efficiency.

Recent advances in fault detection techniques for photovoltaic systems: An overview, classification and performance evaluation

Photovoltaic (PV) arrays are typically built outdoors in severe conditions and are vulnerable to a variety of defects, which will negatively impact their efficiency and reduce the system's performance as a whole. Therefore, implementing efficient fault identification and diagnosis is crucial to increase the productivity and efficiency of PV installations, ensure their safe operation and reduce maintenance costs. In this article, the types and causes of numerous faults that arise in PV systems are swiftly examined. Additionally, a number of the most recent methods suggested in the literature for PV fault diagnostics are reviewed and assessed. We only cover methods for locating faults on the DC side of the PV system. Based on the review that was conducted, suggestions for the direction of future studies are given. Finally, this document serves as a significant manual that offers relevant information on methods of detection and diagnosis for PV systems.

A universal fault diagnosis framework for marine machinery based on domain adaptation

In the digital and automated era, the significance of machine health monitoring has been amplified, especially for marine machinery, which plays a pivotal role in the sustainable advancement of modern industries. Two salient challenges currently hampering fault diagnostics are spotlighted: firstly, the variability in mechanical operating conditions gives rise to inconsistent data distributions, thereby prompting domain shift. Secondly, unobserved in training datasets, testing scenarios may unveil unpredictable, unknown fault modes, culminating in class discrepancies. To effectively tackle these intertwined challenges, we introduce the Two-Stage Universal Domain Adaptation (TSUDA) — a novel fault diagnosis methodology. TSUDA adeptly differentiates new fault types from labeled known counterparts by leveraging a composite transferability metric. Furthermore, it deploys a parameter-adaptive unsupervised algorithm to discern the count of emerging fault types. Rigorous experiments on datasets from marine diesel engines, emblematic of marine machinery, demonstrated TSUDA's superiority over conventional domain adaptation techniques in diverse diagnostic challenges, registering accuracy rates surpassing 90%. While the effectiveness of TSUDA is authenticated within marine machinery contexts in this investigation, its architecture suggests potential applicability in a broader spectrum of mechanical fault diagnostic scenarios.

Detection of Rotating Stall Inception of Axial Compressors Based on Deep Dilated Causal Convolutional Neural Networks

The anomaly detection of outliers and fault diagnosis in the imperfect time-series data is crucial in aero-engine industry. Early detection of the rotating stall is significant for the active stabilization control of the axial compressors, because the rotating stall and surge can cause enormous vibratory stresses in the blades and gives rise to surge to limit the performance of compressors. An aerodynamic instability inception and short-length-scale periodic anomaly prior to stall onset known as stall inception in axial compressors is observed in aero-engine. Based on deep learning theory, this paper conducts the accurate and rapid detection of stall precursors based on deep neural network. The deep dilated causal convolutional neural network combined with logistic regression(LR) named as LR-WaveNet is applied to the detection and prediction of stall inception in the imperfect time-series data of axial compressors with the rotating stall. The LR-WaveNet model can implement fast anomaly detection and stall prediction in long-time term series data. Furthermore, a single LR-WaveNet can be trained to capture and learn the time-domain statistical characteristics of many different stall inception training data with equal fidelity. The trained LR-WaveNet model can rapidly detect the occurrence of anomaly point and predict the probability of rotating stall and surge in axial compressors as an early warning signal. By comparing with the time domain analysis and the wavelet analysis, the calculation results are represented with experimental data to show the effectiveness and feasibility of the stall detection approach based on LR-WaveNet. Especially, the LR-WaveNet can detect the irregular and imperfect stall inception and capture the small fault features when the characteristics of stall inception sigals or data are not very unobvious, irregular and imperfect. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Note to Practitioners–This paper was motivated by the problem of detecting the stall and surge of axial compressor for aeroengine, but it also applies to other small fault diagnosis with imperfect data or time series prediction problem. The stall Inception is very difficult to detect for the reason of imperfect data with small fault or features. These existing detection methods of stall inception can only be applied under certain conditions, because they can only extract the pre-stall features partly. Meanwhile, the excessive number of compressor sensors is demanded in this method, so the application of these traditional methods are limited in practical situations in the stall inception detection. This paper suggests a new approach using Deep Dilated Causal Convolutional Neural Networks. The deep dilated causal convolutional neural network combined with logistic regression(LR) named as LR-WaveNet is applied to the detection and prediction of stall inception in the imperfect time-series data of axial compressors with the rotating stall. In this paper, the new approach is proved to be suitable and feasible to all the types of stall inception including the irregular stall inception with imperfect data. We then show how the deep learning method can be efficiently applied in the imperfect data situation with small fault and time series prediction. The experiments suggest that this approach is feasible. In future research, we will create the novel approach and theory for the detection of stall inception and small fault diagnostics with the imperfect data.

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.

An Intelligent Fuzzy Logic Automobile Fault Diagnostic System

The advent of intelligent transportation systems and the growing complexity of modern automobiles necessitated innovative approaches to fault detection and diagnostics. This study presents an Intelligent Fuzzy Logic Automobile Fault Diagnostic System. It is designed to enhance the safety and reliability of automotive systems. Fuzzy logic with its ability to handle imprecise and uncertain data is harnessed to develop a robust model capable of identifying and classifying faults in real-time. The system incorporates a hybrid computerized fuzzy system, to aid vehicle owners in identifying issues with their vehicles and providing sound repairs recommendations for any malfunctioning parts. The system was implemented using web technologies; ASP.Net, Bootstrap 3.5, CSS, JavaScript, JQuery and SQL server. The results indicate its potential for widespread adoption, with the ability to reduce accidents and maintenance costs while enhancing the driving experience and provides an accuracy of 73.14% in performance. The Precision 100% and F1 Score 75.72%.

Development of a fault diagnostics and tolerance system: An application to continuous stirred tank reactor

Fault diagnosis and tolerance are crucial for monitoring system health and ensuring stability in industrial processes. Challenges arise in designing fault diagnostic solutions for real-time industrial processes with inherent nonlinear dynamic behaviors, particularly when dealing with multiple operating regions characterized by varying dynamics. This article addresses this challenge and proposes a fault diagnostic and tolerant control scheme for industrial systems. The proposed approach integrates a fuzzy-based realization technique with a subspace-aided methodology to effectively handle the nonlinear dynamic behavior observed across different operational scenarios. A practical solution is presented, significantly reducing the computational burden associated with online diagnostics, as the parity vectors are computed offline using available input–output data for different operating regions. During online diagnostics, only computed parity spaces are used with fuzzy realizations for residual generation, leading to a significant reduction in online computation. Numerical examples demonstrate the effectiveness of the proposed method, achieving a high precision rate in fault diagnostics. Furthermore, the diagnostic methodology is integrated with fault-tolerant control for practical applications, as demonstrated in the application of a continuous stirred tank reactor. This integration enables the system to effectively tolerate faults and ensure sub-optimal operation of the industrial process.

BIM-based automated fault detection and diagnostics of HVAC systems in commercial buildings

In order to meet the growing demand for effective Automated Fault Detection and Diagnostics (AFDD) for HVAC systems, innovative approaches are needed to address limitations in data diversity and access to contextual information. This study introduces a methodology that leverages Building Information Modeling (BIM) to enhance the development of the AFDD model. Feature engineering techniques are utilized to generate dynamic BIM features, compensating for the lack of sensory and contextual data in Building Management Systems (BMS). By integrating AFDD analytics with BIM, a comprehensive digital twin of the facility is created, which enables facility managers to compare, reuse, and develop AFDD models for HVAC systems. The proposed methodology demonstrates the potential of leveraging BIM-based knowledge models to overcome the challenges associated with the limited sensor and contextual information availability by utilizing BIM for feature generation and, conversely, updating the BIM model with AFDD analytics.

Editorial for Vol.31, No.2

The June 2023 (Vol. 31, No. 2) issue of CIT. Journal of Computing and Information Technology brings four papers from the areas of fault diagnostics, network security, data processing, and computer vision.

Unsupervised Bayesian change-point detection approach for reliable prognostics and health management of complex mechanical systems

In the prognostics and health management of mechanical systems, achieving high reliability requires high-quality data for analysis and model training to avoid intensive misalarms and false detections. This research introduces a novel unsupervised Bayesian change-point detection approach specifically tailored to improve the accuracy and dependability of data used in the performance evaluation, fault diagnostics, and predictive maintenance of such systems. By leveraging this method, the precision of change-point detection in operational data is significantly heightened, effectively discerning between normal conditions and potential outliers within the recorded data sequences. The proposed approach replaces the reliance on traditional outlier detection methods with an innovative utilization of prediction error metrics, enriched with statistical analysis to assess and bolster the robustness of the detection algorithm. Conducted case studies on data from the supervisory control and data acquisition (SCADA) system of 92 wind turbines highlight the advanced data cleaning capabilities of our method, which markedly outperforms previous techniques including quartile, Gaussian mixture models, isolation forests, and local outlier factor algorithms. The findings illustrate the methodology’s efficacy in obtaining rcleaned data, which are crucial for the reliable prognostics and health management frameworks of mechanic system, and enhance the confidence in feature extraction and performance prediction.

Deep Learning Approach to Within-Bank Fault Detection and Diagnostics of Fine Motion Control Rod Drives

Control rod motion is one of the primary means of regulating the rate of fission in a nuclear reactor core to ensure safe and stable operation. Reactor power distribution and thermal power output can be fine-tuned by adjusting the control rod position. For high-precision control of rod movements, Fine Motion Control Rod Drives (FMCRDs) are often used. The operation of FMCRDs provides a unique opportunity to implement condition monitoring related to the intermittency of motion and the use of control rod banks. This research sets out to detect three types of faults in an electrically driven FMCRD. In addition to detecting faults, this work will attempt to determine both the type of fault and the source of each fault, completing the fault detection and diagnostics (FDD) pipeline on a scarcely researched system. The three types of faults to be investigated are short-circuit faults, ball screw wear faults, and ball screw jam faults. This is a potential advancement to the within-bank FDD of this specific drive system intended for deployment in an advanced nuclear reactor plant. Using encoder-decoder structured convolutional neural networks and autoencoders, the three tested faults were confidently detected and isolated as well as reasonably diagnosed by monitoring the FMCRD servomotor torque.

Detection and Diagnostics of Bearing and Gear Fault under Variable Speed and Load Conditions Using Heterogeneous Signals

In industrial applications, rotating machines operate under real-time variable speed and load regimes. In the presence of faults, the degradation of critical components is accelerated significantly. Therefore, robust monitoring algorithms able to identify these faults become crucial. In the literature, it is hard to find comprehensive monitoring systems that include variable speed and load regimes with combined gearbox faults using electrical and vibration signals. For this purpose, a novel signal processing methodology including a geometric classification technique is proposed. This methodology is based on using different types of sensors such as current, voltage and vibration sensors with a regime normalization, which allows the grouping of different regimes belonging to the same health state. It consists of reducing dispersion between the class observations and separating other classes representing different health states including the variation in speed and load. Then, a peripheral threshold is proposed in our classifier to diagnose new health states. To verify the effectiveness of the methodology, current, voltage and vibration data from a gearbox system are collected under variable speed and load levels.

Assessment of the Performance and Defect Investigation of PV Modules after Accelerated Ageing Tests

Photovoltaic (PV) modules' degradation behaviour, together with outdoor field condition and fault diagnostics, consist valuable data in evaluating efficiency and establishing long-term reliability of a PV system. However, since PV modules commonly come with 25 (even over) years of warranty, testing modules in field for that long period is not feasible. Thus, due to time constraints, many accelerated aging tests have been recently introduced for testing reliability and durability of PV modules. Scope of the presented study is the assessment of the performance degradation and fault (defect) propagation mechanisms with regard to three monocrystalline silicon (mc-Si) PV modules, through a specific thermal cycle accelerated ageing test. The parameters that were experimentally evaluated towards the accelerated ageing process of this study, were: i) the thermal signature evolution and/or propagation of any diagnosed defects, especially in two modules, by means of infrared (IR) thermographic measurements, ii) the overall performance degradation of each module, by means of electrical I-V measurements, together with iii) specific morphological characteristics, by means microscopy imaging. The intended study gave useful and promising results in assessing the effect of the applied thermal cycles upon the modules’ thermal behaviour, electrical efficiency and morphology.

Fault Diagnostics Based on the Analysis of Probability Distributions Estimated Using a Particle Filter

This paper proposes a monitoring procedure based on characterizing state probability distributions estimated using particle filters. The work highlights what types of information can be obtained during state estimation and how the revealed information helps to solve fault diagnosis tasks. If a failure is present in the system, the output predicted by the model is inconsistent with the actual output, which affects the operation of the estimator. The heterogeneity of the probability distribution of states increases, and a large proportion of the particles lose their information content. The correlation structure of the posterior probability density can also be altered by failures. The proposed method uses various indicators that characterize the heterogeneity and correlation structure of the state distribution, as well as the consistency between model predictions and observed behavior, to identify the effects of failures.The applicability of the utilized measures is demonstrated through a dynamic vehicle model, where actuator and sensor failure scenarios are investigated.

Machine Learning-Based Automated Fault Detection and Diagnostics in Building Systems

Automated fault detection and diagnostics analysis in commercial building systems using machine learning (ML) can improve the building’s efficiency and conserve energy costs from inefficient equipment operation. However, ML can be challenging to implement in existing systems due to a lack of common data standards and because of a lack of building operators trained in ML techniques. Additionally, results from ML procedures can be complicated for untrained users to interpret. Boolean rule-based analysis is standard in current automated fault detection and diagnostics (AFDD) solutions but limits analysis to the rules defined and calibrated by energy engineers. Boolean rule-based analysis and ML can be combined to create an effective fault detection and diagnostics (FDD) tool. Three examples of ML’s advantages over rule-based analysis are explored by analyzing functional building equipment. ML can detect long-term faults in the system caused by a lack of system maintenance. It can also detect faults in system components with incomplete sets of sensors by modeling expected system operations and by making comparisons to actual system operations. An example of ML detecting a failure in a building is shown along with a demonstration of the soft decision boundaries of ML-based FDD compared to Boolean rule-based FDD analysis. The results from the three examples are used to demonstrate the strengths and weaknesses of using ML for AFDD analysis.

Integrated building fault detection and diagnosis using data modeling and Bayesian networks

Heating, ventilation, and air-conditioning (HVAC) equipment faults and operational errors result in comfort issues and waste of energy in buildings. An Automatic Fault Detection and Diagnosis (AFDD) tool could help facility managers fix comfort and energy issues more efficiently, by identifying the most probable root causes. Existing AFDD methods mostly focus on equipment-level fault detection and diagnostics; almost no attention is given to building level fault diagnosis, considering inter-dependency between equipment through the energy distribution chain. In this work we propose a methodology to automatically derive a Bayesian network from HVAC system topology description such as Haystack. This Bayesian network models and estimates the state of all elements in the system, helping users to identify the most probable root fault. As it is able to ingest evidence from any source (field data, operators, or other models) and is capable of updating its estimates when new evidence is delivered, such a tool could have a great potential to be used interactively on the field. We applied the proposed methodology on simulated and real-world buildings and present in this paper one specific case.

Model-Driven Bayesian Network Learning for Factory-Level Fault Diagnostics and Resilience

We propose to use engineering models for Bayesian Network (BN) learning for fault diagnostics at the factory-level using key performance indicators (KPIs) such as overall equipment effectiveness (OEE). OEE is widely used in industry and it measures sustainability by capturing product quality (e.g., less scrap) and measures resilience by capturing availability. A major advantage of the proposed approach is that the engineering models are likely to be available long before the corresponding digitalized smart factory becomes fully operational. Specifically, for BN structure learning, we propose to use analytical queueing theory models of the factory to elicit the structure, and to carry out intervention we propose to use designed experiments based on discrete-event simulation models of the factory. For parameter learning, we apply a qualitative maximum a posteriori (QMAP) method and propose additional expert constraints based on the law of propagation of uncertainty from queueing theory. Furthermore, the proposed approach overcomes the challenge of obtaining balanced-class data in BN learning for fault diagnostics. We apply the proposed BN learning approach to (i) a 4-robot cell in our laboratory and (ii) a robotic machining cell in a commercial vehicle factory. In both cases, the proposed method is found to be efficacious in accurately learning the BN structure and parameter, as measured using structural-hamming distance and Kullback–Leibler divergence score, respectively. The proposed approach can pave the way for a new class of resilient and sustainable smart manufacturing systems.

Aeroengine Actuator Fault Detection and Estimation Via Combined Model Observers

Accurate diagnosis of faults in aeroengines is crucial for enhancing reliability and reducing operational and maintenance costs. This paper proposes a fault diagnosis approach for aeroengine actuators that utilizes a combination of model observers. Firstly, the faults of aeroengine actuators are described, and their representation as Aero-engine State Variable Model (SVM) is elaborated on. Secondly, a Luneberger model observer is designed to detect actuator faults using observer residuals. Thirdly, Adaptive Thau Observers (ATO) are developed to accurately estimate the offset value of actuator faults. Finally, the proposed fault diagnostics solution is tested and validated using a benchmark aeroengine model-CMAPSS 40. Fault diagnostics results under two fault scenarios demonstrate the Effectiveness of the proposed approach.