Research

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22 pages, 3395 KiB

Open AccessArticle

Yard Crane Rescheduling under the Influence of Random Fault

by Lin Yao, Hongxing Zheng, Yiran Liu, Danmeng Li and Yilan Zhao

Machines 2023, 11(6), 580; https://doi.org/10.3390/machines11060580 - 23 May 2023

Cited by 1 | Viewed by 879

In the operation of the imported container area of the container yard, the fault of the yard crane often occurs, and the fault is random and unpredictable, which greatly affects the operational efficiency of the container yard. To improve the operation efficiency of [...] Read more.

In the operation of the imported container area of the container yard, the fault of the yard crane often occurs, and the fault is random and unpredictable, which greatly affects the operational efficiency of the container yard. To improve the operation efficiency of the container yard, this paper studies the rescheduling optimization problem of the multi-container area and multi-yard crane when random faults occur in container lifting operations in the container import area. Considering the different impacts of different fault conditions on the container yard operation, the fault impact judgment mechanism is established. The waiting time of external container trucks and customer satisfaction is considered for yard crane rescheduling. Yard crane rescheduling model after the fault is constructed, aiming at the minimum deviation from the original scheduling scheme. And the AEA (annealing evolution algorithm) algorithm is used to solve it. The effectiveness of magic and the specificity of the algorithm are verified by the analysis of numerical examples in different scales. The research data of Dalian Port is used to carry out experiments, and the experimental analysis of examples in different scales verifies the effectiveness of the model and the scientific nature of the algorithm. Compared with the existing scheme, this scheme is more practical, which can not only give the treatment scheme immediately when the fault occurs but also effectively improve the working efficiency of the container yard and provide a reference for the port to enhance customer satisfaction. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis and Prognostics of Machines: Applications and Advances)

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29 pages, 8584 KiB

Open AccessEditor’s ChoiceArticle

Compound Uncertainty Quantification and Aggregation for Reliability Assessment in Industrial Maintenance

by Alex Grenyer, John Ahmet Erkoyuncu, Sri Addepalli and Yifan Zhao

Machines 2023, 11(5), 560; https://doi.org/10.3390/machines11050560 - 16 May 2023

Viewed by 1206

Abstract

The mounting increase in the technological complexity of modern engineering systems requires compound uncertainty quantification, from a quantitative and qualitative perspective. This paper presents a Compound Uncertainty Quantification and Aggregation (CUQA) framework to determine compound outputs along with a determination of the greatest [...] Read more.

The mounting increase in the technological complexity of modern engineering systems requires compound uncertainty quantification, from a quantitative and qualitative perspective. This paper presents a Compound Uncertainty Quantification and Aggregation (CUQA) framework to determine compound outputs along with a determination of the greatest uncertainty contribution via global sensitivity analysis. This was validated in two case studies: a bespoke heat exchanger test rig and a simulated turbofan engine. The results demonstrated the effective measurement of compound uncertainty and the individual impact on system reliability. Further work will derive methods to predict uncertainty in-service and the incorporation of the framework with more complex case studies. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis and Prognostics of Machines: Applications and Advances)

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25 pages, 1586 KiB

Open AccessArticle

Data-Driven Fault Detection of AUV Rudder System: A Mixture Model Approach

by Zhiteng Zhang, Xiaofang Zhang, Tianhong Yan, Shuang Gao and Ze Yu

Machines 2023, 11(5), 551; https://doi.org/10.3390/machines11050551 - 13 May 2023

Viewed by 1733

Abstract

Based on data-driven and mixed models, this study proposes a fault detection method for autonomous underwater vehicle (AUV) rudder systems. The proposed method can effectively detect faults in the absence of angle feedback from the rudder. Considering the parameter uncertainty of the AUV [...] Read more.

Based on data-driven and mixed models, this study proposes a fault detection method for autonomous underwater vehicle (AUV) rudder systems. The proposed method can effectively detect faults in the absence of angle feedback from the rudder. Considering the parameter uncertainty of the AUV motion model resulting from the dynamics analysis method, we present a parameter identification method based on the recurrent neural network (RNN). Prior to identification, singular value decomposition (SVD) was chosen to denoise the original sensor data as the data pretreatment step. The proposed method provides more accurate predictions than recursive least squares (RLSs) and a single RNN. In order to reduce the influence of sensor parameter errors and prediction model errors, the adaptive threshold is mentioned as a method for analyzing prediction errors. In the meantime, the results of the threshold analysis were combined with the qualitative force analysis to determine the rudder system’s fault diagnosis and location. Experiments conducted at sea demonstrate the feasibility and effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis and Prognostics of Machines: Applications and Advances)

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30 pages, 56743 KiB

Open AccessEditor’s ChoiceArticle

Fault Diagnosis of Rotating Machinery Based on Two-Stage Compressed Sensing

by Xianglong You, Jiacheng Li, Zhongwei Deng, Kai Zhang and Hang Yuan

Machines 2023, 11(2), 242; https://doi.org/10.3390/machines11020242 - 06 Feb 2023

Cited by 4 | Viewed by 1221

Abstract

Intelligent on-site fault diagnosis and professional vibration analysis are essential for the safety and stability of rotating machinery operation. This paper represents a fault diagnosis scheme based on two-stage compressed sensing for triaxial vibration data, which realizes fault diagnosis for rotating machinery based [...] Read more.

Intelligent on-site fault diagnosis and professional vibration analysis are essential for the safety and stability of rotating machinery operation. This paper represents a fault diagnosis scheme based on two-stage compressed sensing for triaxial vibration data, which realizes fault diagnosis for rotating machinery based on compressed data and data reconstruction for professional vibration analysis. In the 1st stage, the triaxial vibration signals are compressed using a pre-designed hybrid measurement matrix; these compressed data can be used both for time-frequency transform and for vibration data reconstruction. In the 2nd stage, the frequency spectra of the triaxial vibration signals are fused and further compressed using another pre-designed joint measurement matrix, which inhibits the high-frequency noises simultaneously. Finally, the fused spectra are employed as feature vectors in sparse-representation-based classification, where the proposed batch matching pursuit (BMP) algorithm is utilized to calculate the sparse vectors. The two-stage compression scheme and the BMP algorithm minimize the computational cost of on-site fault diagnosis, which is suitable for edge computing platforms. Meanwhile, the compressed vibration data can be reconstructed, which provides evidence for professional vibration analysis. The method proposed in this study is validated by two practical case studies, in which the accuracies are 99.73% and 96.70%, respectively. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis and Prognostics of Machines: Applications and Advances)

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24 pages, 5463 KiB

Open AccessArticle

Fault Detection in Aircraft Flight Control Actuators Using Support Vector Machines

by Julianne Grehan, Dmitry Ignatyev and Argyrios Zolotas

Machines 2023, 11(2), 211; https://doi.org/10.3390/machines11020211 - 02 Feb 2023

Cited by 2 | Viewed by 2877

Abstract

Future generations of flight control systems, such as those for unmanned autonomous vehicles (UAVs), are likely to be more adaptive and intelligent to cope with the extra safety and reliability requirements due to pilotless operations. An efficient fault detection and isolation (FDI) system [...] Read more.

Future generations of flight control systems, such as those for unmanned autonomous vehicles (UAVs), are likely to be more adaptive and intelligent to cope with the extra safety and reliability requirements due to pilotless operations. An efficient fault detection and isolation (FDI) system is paramount and should be capable of monitoring the health status of an aircraft. Historically, hardware redundancy techniques have been used to detect faults. However, duplicating the actuators in an UAV is not ideal due to the high cost and large mass of additional components. Fortunately, aircraft actuator faults can also be detected using analytical redundancy techniques. In this study, a data-driven algorithm using Support Vector Machine (SVM) is designed. The aircraft actuator fault investigated is the loss-of-effectiveness (LOE) fault. The aim of the fault detection algorithm is to classify the feature vector data into a nominal or faulty class based on the health of the actuator. The results show that the SVM algorithm detects the LOE fault almost instantly, with an average accuracy of 99%. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis and Prognostics of Machines: Applications and Advances)

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21 pages, 11083 KiB

Open AccessArticle

A Deep-Learning-Based Approach for Aircraft Engine Defect Detection

by Anurag Upadhyay, Jun Li, Steve King and Sri Addepalli

Machines 2023, 11(2), 192; https://doi.org/10.3390/machines11020192 - 01 Feb 2023

Cited by 2 | Viewed by 3064

Abstract

Borescope inspection is a labour-intensive process used to find defects in aircraft engines that contain areas not visible during a general visual inspection. The outcome of the process largely depends on the judgment of the maintenance professionals who perform it. This research develops [...] Read more.

Borescope inspection is a labour-intensive process used to find defects in aircraft engines that contain areas not visible during a general visual inspection. The outcome of the process largely depends on the judgment of the maintenance professionals who perform it. This research develops a novel deep learning framework for automated borescope inspection. In the framework, a customised U-Net architecture is developed to detect the defects on high-pressure compressor blades. Since motion blur is introduced in some images while the blades are rotated during the inspection, a hybrid motion deblurring method for image sharpening and denoising is applied to remove the effect based on classic computer vision techniques in combination with a customised GAN model. The framework also addresses the data imbalance, small size of the defects and data availability issues in part by testing different loss functions and generating synthetic images using a customised generative adversarial net (GAN) model, respectively. The results obtained from the implementation of the deep learning framework achieve precisions and recalls of over 90%. The hybrid model for motion deblurring results in a 10× improvement in image quality. However, the framework only achieves modest success with particular loss functions for very small sizes of defects. The future study will focus on very small defects detection and extend the deep learning framework to general borescope inspection. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis and Prognostics of Machines: Applications and Advances)

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30 pages, 3392 KiB

Open AccessEditor’s ChoiceArticle

Explainable Data-Driven Method Combined with Bayesian Filtering for Remaining Useful Lifetime Prediction of Aircraft Engines Using NASA CMAPSS Datasets

by Faisal Maulana, Andrew Starr and Agusmian Partogi Ompusunggu

Machines 2023, 11(2), 163; https://doi.org/10.3390/machines11020163 - 24 Jan 2023

Cited by 3 | Viewed by 2267

Abstract

An aircraft engine is expected to have a high-reliability system as a safety-critical asset. A scheduled maintenance strategy based on statistical calculation has been employed as the current practice to achieve the reliability requirement. Any improvement to this maintenance interval is made after [...] Read more.

An aircraft engine is expected to have a high-reliability system as a safety-critical asset. A scheduled maintenance strategy based on statistical calculation has been employed as the current practice to achieve the reliability requirement. Any improvement to this maintenance interval is made after significant reliability issues arise (such as flight delays and high component removals). Several publications and research studies have been conducted related to this issue, one of them involves performing simulations and providing aircraft operation datasets. The recently published NASA CMAPPS datasets have been utilised in this paper since they simulate flight data recording from various measurements. A prognostics model can be developed by analysing these datasets and predicting the engine’s reliability before failure. However, the state-of-the-art prognostics techniques published in the literature using these NASA CMAPPS datasets are mainly purely data-driven. These techniques mainly deal with a “black box” process which does not include uncertainty quantification (UQ). These two factors are barriers to prognostics applications, particularly in the aviation industry. To tackle these issues, this paper aims at developing explainable and transparent algorithms and a software tool to compute the engine health, estimate engine end of life (EoL), and eventually predict its remaining useful life (RUL). The proposed algorithms use hybrid metrics for feature selection, employ logistic regression for health index estimation, and unscented Kalman filter (UKF) to update the prognostics model for predicting the RUL in a recursive fashion. Among the available datasets, dataset 02 is chosen because it has been widely used and is an ideal candidate for result comparison and dataset 03 is employed as a new state-of-the-art. As a result, the proposed algorithms yield 34.5–55.6% better performance in terms of the root mean squared error (RMSE) compared with the previous work. More importantly, the proposed method is transparent and it quantifies the uncertainty during the prediction process. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis and Prognostics of Machines: Applications and Advances)

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24 pages, 19586 KiB

Open AccessArticle

Detection of Compound Faults in Ball Bearings Using Multiscale-SinGAN, Heat Transfer Search Optimization, and Extreme Learning Machine

by Venish Suthar, Vinay Vakharia, Vivek K. Patel and Milind Shah

Machines 2023, 11(1), 29; https://doi.org/10.3390/machines11010029 - 26 Dec 2022

Cited by 30 | Viewed by 2149

Abstract

Intelligent fault diagnosis gives timely information about the condition of mechanical components. Since rolling element bearings are often used as rotating equipment parts, it is crucial to identify and detect bearing faults. When there are several defects in components or machines, early fault [...] Read more.

Intelligent fault diagnosis gives timely information about the condition of mechanical components. Since rolling element bearings are often used as rotating equipment parts, it is crucial to identify and detect bearing faults. When there are several defects in components or machines, early fault detection becomes necessary to avoid catastrophic failure. This work suggests a novel approach to reliably identifying compound faults in bearings when the availability of experimental data is limited. Vibration signals are recorded from single ball bearings consisting of compound faults, i.e., faults in the inner race, outer race, and rolling elements with a variation in rotational speed. The measured vibration signals are pre-processed using the Hilbert–Huang transform, and, afterward, a Kurtogram is generated. The multiscale-SinGAN model is adapted to generate additional Kurtogram images to effectively train machine-learning models. To identify the relevant features, metaheuristic optimization algorithms such as teaching–learning-based optimization, and Heat Transfer Search are applied to feature vectors. Finally, selected features are fed into three machine-learning models for compound fault identifications. The results demonstrate that extreme learning machines can detect compound faults with 100% Ten-fold cross-validation accuracy. In contrast, the minimum ten-fold cross-validation accuracy of 98.96% is observed with support vector machines. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis and Prognostics of Machines: Applications and Advances)

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19 pages, 5826 KiB

Open AccessEditor’s ChoiceArticle

Signal Processing of Acoustic Data for Condition Monitoring of an Aircraft Ignition System

by Umair Ahmed, Fakhre Ali and Ian Jennions

Machines 2022, 10(9), 822; https://doi.org/10.3390/machines10090822 - 19 Sep 2022

Cited by 2 | Viewed by 2606

Abstract

Degradation of the ignition system can result in startup failure in an aircraft’s auxiliary power unit. In this paper, a novel acoustics-based solution that can enable condition monitoring of an APU ignition system was proposed. In order to support the implementation of this [...] Read more.

Degradation of the ignition system can result in startup failure in an aircraft’s auxiliary power unit. In this paper, a novel acoustics-based solution that can enable condition monitoring of an APU ignition system was proposed. In order to support the implementation of this research study, the experimental data set from Cranfield University’s Boeing 737-400 aircraft was utilized. The overall execution of the approach comprised background noise suppression, estimation of the spark repetition frequency and its fluctuation, spark event segmentation, and feature extraction, in order to monitor the state of the ignition system. The methodology successfully demonstrated the usefulness of the approach in terms of detecting inconsistencies in the behavior of the ignition exciter, as well as detecting trends in the degradation of spark acoustic characteristics. The identified features proved to be robust against non-stationary background noise, and were also found to be independent of the acoustic path between the igniter and microphone locations, qualifying an acoustics-based approach to be practically viable. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis and Prognostics of Machines: Applications and Advances)

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19 pages, 6210 KiB

Open AccessArticle

Intelligent Diagnosis Method for Mechanical Faults of High-Voltage Shunt Reactors Based on Vibration Measurements

by Pengfei Hou, Hongzhong Ma and Ping Ju

Machines 2022, 10(8), 627; https://doi.org/10.3390/machines10080627 - 29 Jul 2022

Cited by 5 | Viewed by 1299

Abstract

Aiming at the difficulty of accurately identifying latent mechanical faults inside high-voltage shunt reactors (HVSRs), this paper proposes a new method for HVSR state feature extraction and intelligent diagnosis. The method integrates a modified complementary ensemble empirical mode decomposition (CEEMD)–permutation entropy–CEEMD (MCPCEEMD) method, [...] Read more.

Aiming at the difficulty of accurately identifying latent mechanical faults inside high-voltage shunt reactors (HVSRs), this paper proposes a new method for HVSR state feature extraction and intelligent diagnosis. The method integrates a modified complementary ensemble empirical mode decomposition (CEEMD)–permutation entropy–CEEMD (MCPCEEMD) method, mutual information theory (MI), multiscale fuzzy entropy (MFE), and an improved grasshopper optimization algorithm to optimize the probabilistic neural network (IGOA-PNN) model. First, we introduce MCPCEEMD for suppressing modal aliasing to decompose the HVSR raw vibration signals. Then, the correlation degree between the obtained intrinsic mode function (IMF) components and the HVSR original vibration signals is judged through MI, and the IMF with the highest correlation is selected for feature extraction. Furthermore, this study uses MFE to quantify the selected IMF. Finally, we employ piecewise inertial weights to improve GOA to select the best smoothing factor for PNN, and use the optimized IGOA-PNN model to identify feature subsets. The experimental results show that the proposed method can successfully diagnose different types and degrees of HVSR mechanical faults, and the identification accuracy rate reaches more than 98%. The high recognition accuracy of the proposed method is helpful for the state detection and field application of HVSRs. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis and Prognostics of Machines: Applications and Advances)

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18 pages, 4971 KiB

Open AccessArticle

Thermal Field and Stress Analysis of Induction Motor with Stator Inter-Turn Fault

by Peng Chen, Ying Xie and Daolu Li

Machines 2022, 10(7), 504; https://doi.org/10.3390/machines10070504 - 23 Jun 2022

Cited by 5 | Viewed by 2611

Abstract

Inter-turn fault (ITF), a typical motor fault, results in significant variations in the thermal characteristics of a motor. For fault, temperature rise (TR) experiments and thermal field-stress field simulations of an induction motor are carried out to reveal the fault characteristics related to [...] Read more.

Inter-turn fault (ITF), a typical motor fault, results in significant variations in the thermal characteristics of a motor. For fault, temperature rise (TR) experiments and thermal field-stress field simulations of an induction motor are carried out to reveal the fault characteristics related to ITF. First, based on the actual structure and the cooling type of the motor, a whole-domain simulation model of the fault thermal field was established. The reasonable equivalence of the motor and the calculation of the heat transfer boundaries were conducted during the modeling process. Then, the three-dimensional transient thermal field under a rated load before and after the fault was obtained, and the accuracy of the simulation could be validated through the comparison of the measured TR at several temperature-measuring points. The heat-transfer law and the notable thermal characteristics of the fault can be presented by analyzing the simulated and measured temperature data. In addition, a fault feature is proposed to provide a reference for diagnosis using the temperature difference of winding at different positions at different moments. Finally, the rotor thermal stress distribution of the normal and faulty motor is obtained by thermal-stress-coupled calculation, which can be used to evaluate the possibility of rotor fault caused by ITF. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis and Prognostics of Machines: Applications and Advances)

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21 pages, 5314 KiB

Open AccessEditor’s ChoiceArticle

Comprehensive and Simplified Fault Diagnosis for Three-Phase Induction Motor Using Parity Equation Approach in Stator Current Reference Frame

by Marco Antonio Rodriguez-Blanco, Victor Golikov, Jose Luis Vazquez-Avila, Oleg Samovarov, Rafael Sanchez-Lara, René Osorio-Sánchez and Agustín Pérez-Ramírez

Machines 2022, 10(5), 379; https://doi.org/10.3390/machines10050379 - 16 May 2022

Cited by 4 | Viewed by 1999

Abstract

In this paper, a complementary and simplified scheme to diagnose electrical faults in a three-phase induction motor using the parity equations approach during steady state operation bases on the stator current reference frame is presented. The proposed scheme allows us to identify the [...] Read more.

In this paper, a complementary and simplified scheme to diagnose electrical faults in a three-phase induction motor using the parity equations approach during steady state operation bases on the stator current reference frame is presented. The proposed scheme allows us to identify the motor phase affected due to faults related to the stator side, such as current sensors, voltage sensors, and resistance. The results obtained in this work complement a detection system that uses the DQ model of the three-phase induction motor and parity equations focused on the synchronous reference frame, which can detect stator-side faults but cannot locate the affected phase. In addition, considering practical and operational aspects, the residual detection set obtained is simplified to three simple algebraic equations that are easy to implement. The simulation results using the PSIM simulation software and the experimental test allow us to validate the proposed scheme. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis and Prognostics of Machines: Applications and Advances)

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Review

Jump to: Research

29 pages, 2634 KiB

Open AccessReview

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

by Jiajin Li, Steve King and Ian Jennions

Machines 2023, 11(4), 481; https://doi.org/10.3390/machines11040481 - 16 Apr 2023

Cited by 4 | Viewed by 3822

Abstract

The fuel system, which aims to provide sufficient fuel to the engine to maintain thrust and power, is one of the most critical systems in the aircraft. However, possible degradation modes, such as leakage and blockage, can lead to component failure, affect performance, [...] Read more.

The fuel system, which aims to provide sufficient fuel to the engine to maintain thrust and power, is one of the most critical systems in the aircraft. However, possible degradation modes, such as leakage and blockage, can lead to component failure, affect performance, and even cause serious accidents. As an advanced maintenance strategy, Condition Based Maintenance (CBM) can provide effective coverage, by combining state-of-the-art sensors with data acquisition and analysis techniques to guide maintenance before the asset’s degradation becomes serious. Artificial Intelligence (AI), particularly machine learning (ML), has proved effective in supporting CBM, for analyzing data and generating predictions regarding the asset’s health condition, thus influencing maintenance plans. However, from an engineering perspective, the output of ML algorithms, usually in the form of data-driven neural networks, has come into question in practice, as it can be non-intuitive and lacks the ability to provide unambiguous engineering signals to maintainers, making it difficult to trust. Engineers are interested in a deterministic decision-making process and how it is being revealed; algorithms should be able to certify and convince engineers to approve recommended actions. Explainable AI (XAI) has emerged as a potential solution, providing some of the logic on how the output is derived from the input given, which may help users understand the diagnostic result of the algorithm. In order to inspire and advise data scientists and engineers who are about to develop and use AI approaches in fuel systems, this paper explores the literature of experiment, simulation, and AI-based diagnostics for the fuel system to make an informed statement as to the progress that has been made in intelligent fault diagnostics for fuel systems, emphasizing the necessity of giving unambiguous engineering signals to maintainers, as well as highlighting potential areas for future research. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis and Prognostics of Machines: Applications and Advances)

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26 pages, 4659 KiB

Open AccessReview

A Review of Diagnostic Methods for Hydraulically Powered Flight Control Actuation Systems

by Samuel David Iyaghigba, Fakhre Ali and Ian K. Jennions

Machines 2023, 11(2), 165; https://doi.org/10.3390/machines11020165 - 25 Jan 2023

Cited by 1 | Viewed by 1923

Abstract

Aircraft systems are designed to perform functions that will aid the various missions of the aircraft. Their performance, when subjected to an unfamiliar condition of operation, imposes stress on them. The system components experience degradation due to fault which ultimately results in failure. [...] Read more.

Aircraft systems are designed to perform functions that will aid the various missions of the aircraft. Their performance, when subjected to an unfamiliar condition of operation, imposes stress on them. The system components experience degradation due to fault which ultimately results in failure. Maintenance and monitoring mechanisms are put in place to ensure these systems are readily available when required. Thus, the sensing of parameters assists in providing conditions under which healthy and faulty scenarios can be indicated. To obtain parameter values, sensor data is processed, and the results are displayed so that the presence of faults may be known. Some faults are intermittent and incipient in nature. These are not discovered easily and can only be known through a display of unusual system performance by error code indication. Therefore, the assessed faults are transmitted to a maintenance crew by error codes. The results may be fault found (FF), no fault found (NFF), or cannot display (CND). However, the main classification of the faults and their origins may not be known in the system. This continues throughout the life cycle of the system or equipment. This paper reviews the diagnostic methods used for the hydraulically powered flight control actuation system (HPFCAS) of an aircraft and its interaction with other aircraft systems. The complexities of the subsystem’s integration are discussed, and different subsystems are identified. Approaches used for the diagnostics of faults, such as model-based, statistical mapping and classification, the use of algorithms, as well as parity checks are reviewed. These are integrated vehicle health management (IVHM) tools for systems diagnostics. The review shows that when a system is made up of several subsystems on the aircraft with dissimilar functions, the probability of fault existing in the system increases, as the subsystems are interconnected for resource sharing, space, and weight savings. Additionally, this review demonstrates that data-driven approaches for the fault diagnostics of components are good. However, they require large amounts of data for feature extraction. For a system such as the HPFCAS, flight-management data or aircraft maintenance records hold information on performance, health monitoring, diagnostics, and time scales during operation. These are needed for analysis. Here, a knowledge of training algorithms is used to interpret different fault scenarios from the record. Thus, such specific data are not readily available for use in a data-driven approach, since manufacturers, producers, and the end users of the system components or equipment do not readily distribute these verifiable data. This makes it difficult to perform diagnostics using a data-driven approach. In conclusion, this paper exposes the areas of interest, which constitute opportunities and challenges in the diagnostics and health monitoring of flight-control actuation systems on aircraft. Full article

(This article belongs to the Special Issue Fault Detection, Diagnosis and Prognostics of Machines: Applications and Advances)

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