Fault Detection And Diagnosis Strategy Research Articles

Advancements in condition monitoring and fault diagnosis of rotating machinery: A comprehensive review of image-based intelligent techniques for induction motors

Recently, condition monitoring (CM) and fault detection and diagnosis (FDD) techniques for rotating machinery (RM) have witnessed substantial advancements in recent decades, driven by the increasing demand for enhanced reliability, efficiency, and safety in industrial operations. CM of valuable and high-cost machinery is crucial for performance tracking, reducing maintenance costs, enhancing efficiency and reliability, and minimizing mechanical failures. While various FDD methods for RM have been developed, these predominantly focus on signal processing diagnostics techniques encompassing time, frequency, and time-frequency domains, intelligent diagnostics, image processing, data fusion, data mining, and expert systems. However, there is a noticeable knowledge gap regarding the specific review of image-based CM and FDD. The objective of this research is to address the aforementioned gap in the literature by conducting a comprehensive review of image-based intelligent techniques for CM and fault FDD specifically applied to induction motors (IMs). The focus of the study is to explore the utilization of image-based methods in the context of IMs, providing a thorough examination of the existing literature, methodologies, and applications. Furthermore, the integration of image-based techniques in CM and FDD holds promise for enhanced accuracy, as visual information can provide valuable insights into the physical condition and structural integrity of the IMs, thereby facilitating early FDD and proactive maintenance strategies. The review encompasses the three main faults associated with IMs, namely bearing faults, stator faults, and rotor faults. Furthermore, a thorough assessment is conducted to analyze the benefits and drawbacks associated with each approach, thereby enabling an evaluation of the efficacy of image-based intelligent techniques in the context of CM and FDD. Finally, the paper concludes by highlighting key issues and suggesting potential avenues for future research.

A Robust Model-Based Strategy for Real-Time Fault Detection and Diagnosis in an Electro-Hydraulic Actuator Using Updated Interactive Multiple Model Smooth Variable Structure Filter

Abstract In industries where harsh environments and stringent safety requirements are prevalent, the widespread use of applications has made it essential to focus on fault detection and diagnosis (FDD) in hydraulic actuators. To achieve this, model-based FDD techniques are utilized, which employ estimation tools like observers and filters. However, for many applications, particularly in fluid power systems, observability, and parameter uncertainty pose constraints to extracting information and estimating parameters. To address these issues, an efficient form of interactive multiple model (IMM), called updated IMM (UIMM), is applied to an electro-hydraulic actuator (EHA) to detect and isolate persisting friction and leakage faults. UIMM method progresses through a series of models that correspond to the fault condition's progression instead of considering all models at once (as is done in IMM). This reduces the number of models running simultaneously, providing two significant benefits: enhanced computational efficiency and avoidance of the combinatorial explosion. The smooth variable structure filter with variable boundary layer (SVSF-VBL) is used for state and parameter estimation in conjunction with UIMM. SVSF-VBL is a reliable suboptimal estimation method that performs better than the Kalman filter regarding uncertainties related to the system and modeling. The performance of the UIMM method is validated by the simulation of fault conditions for a typical EHA. A fault tolerable control system (FTCS) has been designed to demonstrate the use of the proposed FDD strategy for fault management in a closed-loop system.

Advances in Fault Detection and Diagnosis for Thermal Power Plants: A Review of Intelligent Techniques

Thermal power plants (TPPs) are critical to supplying energy to society, and ensuring their safe and efficient operation is a top priority. To minimize maintenance shutdowns and costs, modern TPPs have adopted advanced fault detection and diagnosis (FDD) techniques. These FDD approaches can be divided into three main categories: model-based, data-driven-based, and statistical-based methods. Despite the practical limitations of model-based methods, a multitude of data-driven and statistical techniques have been developed to monitor key equipment in TPPs. The main contribution of this paper is a systematic review of advanced FDD methods that addresses a literature gap by providing a comprehensive comparison and analysis of these techniques. The review discusses the most relevant FDD strategies, including model-based, data-driven, and statistical-based approaches, and their applications in enhancing the efficiency and reliability of TPPs. Our review highlights the novel and innovative aspects of these techniques and emphasizes their significance in sustainable energy development and the long-term viability of thermal power generation. This review further explores the recent advancements in intelligent FDD techniques for boilers and turbines in TPPs. It also discusses real-world applications, and analyzes the limitations and challenges of current approaches. The paper highlights the need for further research and development in this field, and outlines potential future directions to improve the safety, efficiency, and reliability of intelligent TPPs. Overall, this review provides valuable insights into the current state-of-the-art in FDD techniques for TPPs, and serves as a guide for future research and development.

Fault detection and diagnosis using tree-based ensemble learning methods and multivariate control charts for centrifugal chillers

Fault detection and diagnosis (FDD) of centrifugal chillers plays an essential role in reducing energy consumption and ensuring safe operation of heating, ventilation and air conditioning systems. In the existing chiller FDD strategy, Data-driven based system modelling methods and statistical analysis-based detection techniques have been widely discussed and applied in recent years. This paper studies the performance of tree-based ensemble learning methods in chiller modelling and analyses multivariate control charts' detection effect. Predictions from the reference model are used in tandem with multivariate control charts to isolate faulty behavior from normal behavior, and then specific failures are identified through the diagnosis model when anomalies are detected. Firstly, in order to establish the optimal chiller reference model and diagnosis model, three tree-based ensemble learning algorithms, namely (i) random forest (RF), (ii) extreme gradient boosting (XGBoost), and (iii) light gradient boosting machine (LightGBM), are evaluated and compared with commonly used support vector machine (SVM). Secondly, Hotelling's T2, multivariate cumulative sum (MCUSUM) and multivariate exponentially weighted moving average (MEWMA) are introduced and their performance in chiller fault detection is tested. Finally, the proposed LightGBM-MEWMA method is validated by the experimental data of ASHRAE RP-1043. Results show that the proposed method effectively identifies seven common chiller faults. Especially at low severity levels, the average detection rate is 88.71%, and the diagnosis accuracy rate is 82.3%.

Enhancing Interpretability of Data-Driven Fault Detection and Diagnosis Methodology with Maintainability Rules in Smart Building Management

Data-driven fault detection and diagnosis (FDD) methods, referring to the newer generation of artificial intelligence (AI) empowered classification methods, such as data science analysis, big data, Internet of things (IoT), industry 4.0, etc., become increasingly important for facility management in the smart building design and smart city construction. While data-driven FDD methods nowadays outperform the majority of traditional FDD approaches, such as the physically based models and mathematically based models, in terms of both efficiency and accuracy, the interpretability of those methods does not grow significantly. Instead, according to the literature survey, the interpretability of the data-driven FDD methods becomes the main concern and creates barriers for those methods to be adopted in real-world industrial applications. In this study, we reviewed the existing data-driven FDD approaches for building mechanical & electrical engineering (M&E) services faults and discussed the interpretability of the modern data-driven FDD methods. Two data-driven FDD strategies integrating the expert reasoning of the faults were proposed. Lists of expert rules, knowledge of maintainability, international/local standards were concluded for various M&E services, including heating, ventilation air-conditioning (HVAC), plumbing, fire safety, electrical and elevator systems based on surveys of 110 buildings in Singapore. The surveyed results significantly enhance the interpretability of data-driven FDD methods for M&E services, potentially enhance the FDD performance in terms of accuracy and promote the data-driven FDD approaches to real-world facility management practices.

A hybrid piecewise FDD strategy for refrigeration charge fault of airborne vapor-compression cycle system

Many researchers have carried out study on refrigerant charge fault for variable refrigerant flow (VRF) air-conditioning system, while few pay attention to refrigerant charge fault for vapor-compression refrigerant cycle system (VCS) of enhanced vapor injection (EVI) with a flash tank onboard. To develop a strategy for refrigerant charge fault of the above system, the optimization methods have been implemented to predict the refrigerant charge level (RCL) based on virtual refrigerant charge (VRC) models. Firstly, Pearson correlation coefficient analysis is employed to obtain the characteristic parameters. Secondly, a piecewise modified VRC model with the different characteristic parameters from VRC models is obtained for two cases of RCL < 75% and RCL > 75%. Meanwhile, combined with the principal component analysis (PCA) and the support vector machine (SVM) algorithm, a PCA-SVM model is developed to improve the prediction accuracy, and the average root mean squared error (RMSE) is only 7.8 compared to 14.9 of the modified VRC model when RCL > 75%, meanwhile, the correctly detected ratio is improved from 62.8% to 84.5%. Then, for solving the difficulty of fault detection and diagnosis (FDD) when RCL > 160%, the moving time window average (MA) algorithm is introduced and an online PCA-MA-SVM model is developed. Finally, a hybrid piecewise FDD strategy combined with three models for refrigeration charge fault in airborne VCS of EVI with a flash tank is obtained.

An improved incipient whale optimization algorithm based robust fault detection and diagnosis for sensorless brushless DC motor drive under external disturbances

In general, unexpected failures in sensorless brushless DC (BLDC) motors can result in production downtime, costly repairs, and safety concerns. BLDC motors are commonly used in home appliances, the medical sector, aerospace, small-scale, and large-scale industries under uncertain operating conditions. Therefore, the fault detection and diagnosis (FDD) of BLDC motor drives can play a very important role in increasing their performance, reliability, robustness control, and operational safety under uncertain operating conditions in critical real-time applications. To satisfy these issues of hall effect sensor, misplacement of a hall-effect sensor, inverter IGBT open-switch fault diagnosis, failure of hall effect sensor, lack of robustness speed control of BLDC motor, which has received substantial interest in academic and industry sectors to establish the proposed work optimization techniques approach FDD strategy for speed control of sensorless BLDC motor under uncertain operating conditions. The proposed optimization techniques such as Bat Algorithm (BA), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA) approach FDD strategies for BLDC motor drives. These FDD strategies simulated by the above optimization techniques on a sensorless BLDC motor with numerical Matlab/Simulink 2020a simulation results are verified. From the simulation results, out of three optimization techniques, the WOA-based FDD strategies are very effective for both bearing and stator winding faults detection and diagnosis in sensorless BLDC motor drives.

Fault detection and diagnosis for variable-air-volume systems using combined residual, qualitative and quantitative techniques

Due to its complex structure and frequent operation, the variable-air-volume (VAV) system are prone to failure. Since faults in VAV systems can reduce its efficiency and increase its energy consumption dramatically, various fault detection and diagnosis (FDD) strategies have been developed. However, due to the diversity of buildings and system structural complexity, most of these efforts are limited to local scale and fault detection at system level has rarely been considered. In this study, the complexity of VAV system modeling is dispersed with a hierarchical modeling framework, and a system level to component scale FDD strategy for VAV systems using combined residual, qualitative and quantitative techniques is developed. The three layers of the modelling framework, namely system layer, unit layer and component layer, adopt different modeling and fault diagnosis methods to improve the accuracy and reliability of FDD. On the other hand, detecting progressive faults is not easy for VAV systems because their symptoms are non-obvious. This study proposes a control quantity based residual statistics in the unit layer, which detects progressive failures with the residual between fault-free-model-predicted and measured control quantities of the zone thermal system. The proposed method is easy to implement and integrate within existing VAV control systems. The results of experiments and a simulation based on the Skyspark platform data from an office building verify the effectiveness of the approach.

A data-driven approach to simultaneous fault detection and diagnosis in data centers

The failure of cooling systems in data centers (DCs) leads to higher indoor temperatures, causing crucial electronic devices to fail, and produces a significant economic loss. To circumvent this issue, fault detection and diagnosis (FDD) algorithms and associated control strategies can be applied to detect, diagnose, and isolate faults. Existing methods that apply FDD to DC cooling systems are designed to successfully overcome individually occurring faults but have difficulty in handling simultaneous faults. These methods either require expensive measurements or those made over a wide range of conditions to develop training models, which can be time-consuming and costly. We develop a rapid and accurate, single and multiple FDD strategy for a DC with a row-based cooling system using data-driven fault classifiers informed by a gray-box temperature prediction model. The gray-box model provides thermal maps of the DC airspace for single as well as a few simultaneous failure conditions, which are used as inputs for two different data-driven classifiers, CNN and RNN, to rapidly predict multiple simultaneous failures. The model is validated with testing data from an experimental DC. Also, the effect of adding Gaussian white noise to training data is discussed and observed that even with low noisy environment, the FDD strategy can diagnose multiple faults with accuracy as high as 100% while requiring relatively few simultaneous fault training data samples. Finally, the different classifiers are compared in terms of accuracy, confusion matrix, precision, recall and F1-score.

Fault detection and diagnosis for the screw chillers using multi-region XGBoost model

Chillers play essential roles in the heating, ventilation and air conditioning (HVAC) systems to ensure the required thermal comfort. To reduce the operational risk such as faulty operation or energy waste, it’s essential to develop the robust and effective fault detection and diagnosis (FDD) strategy for the chillers. This paper presents a novel hybrid reference model called multi-region XGBoost model that integrates parameter optimized XGBoost model with mean shift clustering method. Based on the reference model, an FDD strategy, including two stages, is proposed. The experiments are carried out on a screw chiller, on which three thermal faults are investigated. The indicative characteristic quantities are selected as the model inputs for detection and diagnosis purpose. The FDD result of the multi-region XGBoost model is compared with that of support vector machine (SVM) model and XGBoost model without clustering. In terms of the performance of fault detection, the multi-region XGBoost model detects 97.26% faulty samples while correctly identifies 99.10% fault-free samples. As for fault diagnosis, the multi-region XGBoost model possesses the highest fault diagnosis accuracy of 96.89%. Besides, the hybrid model also shows the best generalization ability. The FDD result reveals that the multi-region XGBoost model has the reliable efficiency for the screw chiller application.

Application of PSO-LSSVM and hybrid programming to fault diagnosis of refrigeration systems

Fault detection and diagnosis (FDD) in refrigeration systems is of great importance for ensuring better equipment reliability and energy efficiency. Although numerous researches studied FDD algorithms and methodology, there is still a lack of mature commercial software in this field. This study presents a novel hybrid model by introducing particle swarm optimization (PSO) into least squares support vector machine (LSSVM) for parameter optimization to overcome the blindness of parameter selection, and proposes a novel idea of hybrid programming, where MATLAB is used to implement the FDD strategy and LabVIEW is employed for interface creation, to take the advantage of both sides. The hybrid programming is carried out through MATLAB script node, and an FDD platform for refrigeration systems is established. The strategy and the platform is validated using experimental data for a centrifugal chiller, where seven typical faults were investigated. The results show that the proposed PSO-LSSVM achieves an overall diagnostic accuracy of 99.70%, drastically improved (8.81%) from that of the LSSVM without optimization. The idea of hybrid programming is feasible for the establishment of an operable and highly integrated FDD platform with user-friendly interface and extendable functions. The practice of the idea also promotes the possibility of combining FDD with system control for better field applications.

Enhanced Fault Diagnosis Using Broad Learning for Traction Systems in High-Speed Trains

Faults happen inevitably in traction systems and thus place the security of the whole high-speed train at risk. In order to improve the safety and reliability of high-speed trains, this article deals with fault detection and diagnosis (FDD) problem for traction systems. Because of high sampling frequency of equipped sensors, FDD strategies in the supervision system of high-speed trains should be of enough high computation efficiency, which is a great bottleneck for artificial intelligence-based FDD methods. For reducing the computational load while maintaining the satisfactory diagnostic accuracy, an enhanced FDD architecture using the modified principal component analysis and broad learning system is developed in this article. Based on the proposed data-driven design whose core is to extract fault information, fast and accurate FDD can be achieved without requirements for mathematical models or control mechanism of high-speed trains. The effectiveness and feasibility of the proposed online design are illustrated on the traction control platform of high-speed trains.

Unknown input observer design for fault detection and diagnosis in a continuous stirred-tank reactor

Early and accurate fault detection and diagnosis (FDD) minimises downtime, increases the safety and reliability of plant operation, and reduces manufacturing costs. This paper presents a robust FDD strategy for a nonlinear system using a bank of unknown input observers (UIO). The approach is based on structure residual generation that provides not only decoupling of faults from model uncertainties and unknown input disturbance but also decoupling the effect of a fault from the effects of other faults. The generated residual was evaluated through the statistical threshold to avoid fault missing or false alarm. The performance of the robust FDD scheme was assessed by some sensor fault scenarios created in a continuous stirred-tank reactor (CSTR). The simulation result showed the effectiveness of the proposed approach.

Development and Field Evaluation of Data-driven Whole Building Fault Detection and Diagnosis Strategy

Faults, i.e., malfunctioned sensors, components, control, and systems, in a building have significantly adverse impacts on the building’s energy consumption and indoor environment. To date, extensive research has been conducted on the development of component level fault detection and diagnosis (FDD) for building systems, especially the Heating, Ventilating, and Air Conditioning (HVAC) system. However, for faults that have multi-system impacts, component level FDD tools may encounter high false alarm rate due to the fact that HVAC subsystems are often tightly coupled together. Hence, the detection and diagnosis of whole building faults is the focus of this study. Here, a whole building fault refers to a fault that occurs in one subsystem but triggers abnormalities in other subsystems and have significant adverse whole building energy impact. The wide adoption of building automation systems (BAS) and the development of machine learning techniques make it possible and cost-efficient to detect and diagnose whole building faults using data-driven methods. In this study, a whole building FDD strategy which adopts weather and schedule information based pattern matching (WPM) method and feature based Principal Component Analysis (FPCA) for fault detection, as well as Bayesian Networks (BNs) based method for fault diagnosis is developed. Fault tests are implemented in a real campus building. The collected data are used to evaluate the performance of the proposed whole building FDD strategies.

An efficient VRF system fault diagnosis strategy for refrigerant charge amount based on PCA and dual neural network model

A fault detection and diagnosis (FDD) strategy is critical for the refrigerant charge amount (RCA) fault since improper RCA may affect the operational performance of a variable refrigerant flow system. The author’s former work proposes a FDD strategy for the RCA fault. However, three aspects of the former FDD strategy need improvement, i.e. model performance evaluation, more feature information preservation and fault diagnosis accuracy (FDA), especially for the undercharge fault. Firstly, with regard to the model performance evaluation, the concept of a confidence space is proposed to evaluate the FDD model. Secondly, principle component analysis (PCA) is used to reduce the dimension of all feature variables to improve the computational efficiency while preserving almost all feature information. Finally, in order to improve the FDA for the undercharge fault, a dual neural network model for the RCA fault diagnosis strategy has been adopted. The results show that a confidence space can effectively reflect the reliability of fault diagnosis, and the PCA reduces nearly half of the dimension while preserving more than 97% of the feature information, more importantly, the dual neural network improves the correct classification ratio (CCR) more than 9% for three classes (undercharge, normal charge, overcharge), with CCR for the undercharge fault improving by 26.8%.

Fault detection and diagnosis in asymmetric multilevel inverter using artificial neural network

ABSTRACTThe increased component requirement to realise multilevel inverter (MLI) fallout in a higher fault prospect due to power semiconductors. In this scenario, efficient fault detection and diagnosis (FDD) strategies to detect and locate the power semiconductor faults have to be incorporated in addition to the conventional protection systems. Even though a number of FDD methods have been introduced in the symmetrical cascaded H-bridge (CHB) MLIs, very few methods address the FDD in asymmetric CHB-MLIs. In this paper, the gate-open circuit FDD strategy in asymmetric CHB-MLI is presented. Here, a single artificial neural network (ANN) is used to detect and diagnose the fault in both binary and trinary configurations of the asymmetric CHB-MLIs. In this method, features of the output voltage of the MLIs are used as to train the ANN for FDD method. The results prove the validity of the proposed method in detecting and locating the fault in both asymmetric MLI configurations. Finally, the ANN response to the input parameter variation is also analysed to access the performance of the proposed ANN-based FDD strategy.

A hybrid ICA-BPNN-based FDD strategy for refrigerant charge faults in variable refrigerant flow system

Refrigerant charge fault is inevitable in air conditioning systems causing serious negative influences on system performance. This study presented a hybrid ICA-BPNN-based fault detection and diagnosis (FDD) strategy for refrigerant charge faults in variable refrigerant flow (VRF) system. It consists of two steps. Firstly, the independent component analysis (ICA) method is employed to detect the faults, and the normal-charge operating data set is used to train the ICA model. Secondly, a fault diagnosis model is established using the back-propagation neural network (BPNN) method, and the BPNN model is trained by the faulty operating data set with labels. The results show that the original data dimensions are reduced from 12 to 4 by the ICA algorithm. ICA-based method can detect the faults using both I2-statistic and I2-SPE-statistic. The accuracy rates are 93.6% and 95.9% respectively. Combined with the BPNN model, the hybrid ICA-BPNN model shows good fault diagnosis performance. Compared with single BPNN method, the hybrid model improves the accuracy rates from 82.7% to 93.8% for overcharge fault data using I2-SPE-statistic.

Modularized PCA method combined with expert-based multivariate decoupling for FDD in VRF systems including indoor unit faults

Developing fault detection and diagnosis (FDD) for the variable refrigerant flow (VRF) system is very important for saving energy and improving reliability of the equipment. For indoor unit (IDU) faults of VRF system, it is especially necessary to detect faults and identify which IDU is faulty. Therefore, this paper has proposed a fault detection strategy based on modularized PCA method, which cannot only detect faults but also specify the faulty IDU. Fault detection models are established respectively for outdoor unit (ODU) and IDUs of VRF system using the modularized PCA method. Then, the expert-based multivariate decoupling strategy with six variables for VRF system is developed to isolate faults. Four common faults are taken into account for VRF system, which include two IDUs faults (electronic expansion valve fault and IDU air-side fouling), one ODU fault (reversing valve stick) and one system fault (refrigerant undercharge). The proposed FDD strategy is evaluated by experimental data of four faults. The test results have shown that modularized PCA-based fault detection method and rule-based diagnosis method are effective for the four typical faults in VRF system. Therefore, it is quite suitable for FDD of VRF system.

Comparative investigations on reference models for fault detection and diagnosis in centrifugal chiller systems

A successful fault detection and diagnosis (FDD) strategy for chillers is highly dependent on three core aspects, an accurate reference model, a sensitive fault detection technique and an effective fault identification method. An accurate model plays an important role in the performance of chiller FDD strategy. In this study, the accuracies of three models, MLR (multiple linear regression), KRG (Kriging) and RBF (radial basis function), are compared under various modeling criteria. The EWMA (exponentially-weighted moving average) residual control chart, which is a more powerful tool in detecting small shifts than t-statistics based method, is adopted as a sensitive fault detection technique. The most sensitive feature parameter method is improved to effectively identify faults. Seven common faults in typical centrifugal chillers are considered. Three strategies (MLR-EWMA, KRG-EWMA and RBF-EWMA), which are the combinations of three reference models with EWMA and the most sensitive feature parameter method, are validated and compared by using two sample datasets with different sizes from ASHRAE RP-1043. Through the comparisons of diagnosis performance, the most accurate chiller reference model is found and suggested to detect and diagnose faults in chiller systems. The comparison results show that the RBF-based strategy has the best diagnosis performances among the three strategies.

An enhanced chiller FDD strategy based on the combination of the LSSVR-DE model and EWMA control charts

Development of the fault detection and diagnosis (FDD) for chiller systems is very important for improving the equipment reliability and saving energy consumption. The results of FDD performance are strongly dependent on the accuracy of chiller models. Since the accuracy of the chiller models depends on the indefinite model parameters which are normally chosen by experiments or experiences, an accurate chiller model is difficult to build. Therefore, optimization of model parameters is very useful to increase the accuracy of chiller models. This paper presents a new FDD strategy for centrifugal chillers of building air-conditioning systems, which is the combination between the nonlinear least squares support vector regression (LSSVR) based on the differential evolution (DE) algorithm and the exponentially weighted moving average (EWMA) control charts. In this strategy, the nonlinear LSSVR, which is a reformulation of SVR model with better generalization performances, is adopted to develop the reference feature parameter models in a typical non-linear chiller system. The DE algorithm which is a real-coding optimal algorithm with powerful global searching capacity is employed to enhance the accuracy of LSSVR models. The exponentially weighted moving average (EWMA) control charts are introduced to improve the fault detection capability as well as to reduce the Type II errors in a t-statistics-based way. Six typical faults of the chiller from the real-time experimental data of ASHRAE RP-1043 project are chosen to validate proposed FDD methods. Comprehensive comparisons between the proposed method and two similarly previous studies are performed. The comparison results show that the proposed method has achieved significant improvement in accuracy and reliability, especially at low severity levels. The proposed DE-LSSVR-EWMA strategy is robust for fault detection and diagnosis in centrifugal chiller systems.