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• Development of a multi-attribute Bayesian control scheme for fault prediction. • Multivariate hidden Markov model describes the deterioration process. • Computational algorithm to optimize the multi-attribute Bayesian policy. • Development of a real case study and verification using multivariate data. Although Bayesian approaches have been utilized in engineering systems for health prognostics, very little work has been done using Bayesian methods for fault prediction of systems under multiple attributes. To address this issue, in this paper a novel multi-attribute Bayesian control chart is presented for predicting failures of hidden-state systems by jointly considering two performance measures of system operation. The system actual status is represented by a three-state multivariate hidden stochastic process with a normal state, an abnormal state, and a failure state. The working states are unobservable and failure state is observable. Based on the built hidden-state model, a fault prediction scheme integrating both system availability and cost objectives is constructed via a multi-attribute Bayesian control chart to monitor and predict impending risks of the operational systems. The Bayesian control chart alarms when the probability of impending risks reaches a certain control limit, which is optimized and determined by a computational algorithm developed in a semi-Markov decision process framework. The proposed fault prediction scheme provides an appearing feature to jointly consider multiple attributes for hidden-state systems. A real case study of mechanical generators is presented and a comparison with other Bayesian and non-Bayesian methods is also given, which demonstrates the effectiveness and superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

• Competing risks model with aging and continuous-state degradation. • Development of prognostic approach with dynamic corrective mechanisms. • Development of corrected residual life-based maintenance policy with dynamic control policy. • Development of SMDP approach to optimize the multi-objective maintenance model. • Considerably better MRL and maintenance performance compared with published results. The accurate prediction of residual life in deteriorating systems is a challenging task due to uncertainties and idealized assumptions during data acquisition and model construction. To address this challenge, the paper proposes a bi-level maintenance model that incorporates a corrected residual life (CRL) approach for a deteriorating system under competing risks. Specifically, the degradation failure is modeled using a Gamma process, while the competing sudden failure is described by a proportional hazards (PH) model. To reduce inconsistencies in residual life prediction, a CRL model is presented and continuously updated with a dynamic corrective factor at each monitoring epoch. Based on the updated CRL, a bi-level maintenance model is developed, which considers both system availability and maintenance cost objectives, enabling dynamic monitoring of the system's degradation state and residual life. The optimal decision variables in the maintenance model are determined through a multi-objective optimization algorithm formulated within a semi-Markov decision process (SMDP) framework. The unique aspect of this work lies in the consideration of prediction errors in residual life and the incorporation of multi-attribute optimization for maintenance design. The proposed method is validated through a case study on light-emitting diodes, confirming its effectiveness in improving residual life prediction and optimizing maintenance decisions. [ABSTRACT FROM AUTHOR]

A general methodology for the dynamic modeling and analysis of planar flexible deployable structures consisting of scissor-like elements (SLEs) is presented. This modeling method is based on a comprehensive consideration of the symmetry and array characteristics of deployable structure and on an improved absolute node coordinate formulation (ANCF), which can model the warping of beam section using the locking-free shear deformable beam element. An effective node separation method is proposed to reduce the number of degrees of freedom of the dynamic equation in the ANCF framework, eliminate the constraint equations within and between SLEs and obtain a compact matrix. This reduction method has good adaptability and can be extended to all kinds of scissor deployable structures with array characteristics, whether they are planar or spatial structures. In addition, the modified generalized α method is utilized to solve the motion equations of deployable structure and eliminate the false high-frequency response generated in the calculation process. Finally, the methodology is validated using a cantilever beam case, and the parametric dynamic response of 2 × 2 and 1 × 2 deployable structures is implemented in this paper. The obtained results show that flexibility has an important impact on the dynamic characteristics of large deployable structures, and the deployable structure is unstable near 0 ∘ and 90 ∘ , and its safe working angle is 17 ∘ – 75 ∘ . It is necessary to carry out parametric research on this structures in the design stage because the prediction of these parameters such as initial configuration and component materials can improve the stability and deployment accuracy of deployable structures in orbit. [ABSTRACT FROM AUTHOR]

Generally, the internal states of most engineering systems are inaccessible to sensors under operational conditions, permitting indirect measurements and failures to be observed discretely. For this type of system, a class of proportional hazard (PH) models with multistate process have been proposed for performing prognostics. However, existing studies use a constant degradation mechanism with a very limited number of states, which may fail to model the actual deterioration of the system. Therefore, this article presents a novel PH model that utilizes a nonconstant degradation mechanism with a large number of states to perform health prognostics. A matrix partition-based approximation method is developed to include all the possible state transitions and to estimate important health quantities of the system, such as conditional reliability (CR), mean remaining useful life (MRUL), and remaining useful life distribution (RULD). An online prognostic scheme based on this matrix approach is presented to provide an update with the latest health quantities of the system. The proposed approach provides appealing computational features and avoids difficulties in calculating uncountable transitions for PH model-based prognostics. The effectiveness of the proposed method is demonstrated via a case of helical gearboxes under indirect monitoring and random failure conditions. [ABSTRACT FROM AUTHOR]

• A novel deterioration-integrated failure model with varying environments. • A matrix-based approximation method for health measures calculation. • An adaptive reliability-based maintenance policy with a switchable control chart. • An efficient computational algorithm to find the optimal decision variables. • Considerable better maintenance performance than published results. The performance of mechanical systems is greatly influenced by varying operational environments such as load, temperature, and humidity, which can result in accelerated degradation and failure. To address this issue, this article considers an adaptive maintenance policy based on updated conditional reliability for mechanical systems under variable environments. A model of deterioration-integrated failure process is presented, which integrates degradation process, failure events, and environmental variables of the system. The degradation process is modeled by a random effect Wiener process, with its drift parameter following a general distribution to represent the environmental random effects. The failure events are modeled by a proportional hazards (PH) model with environment-varying covariate. The health of the system is evaluated using a matrix-based approximation method that provides estimates of the system's conditional reliability and mean residual life. Using the prognostic information, an adaptive maintenance model with a switchable control chart is then developed to monitor and maintain system based on the status of system reliability. The decision variables of the adaptive reliability-based maintenance are determined by a computational algorithm formulated in a semi-Markov decision process (SMDP) framework, with the objective of minimizing the long-run expected average cost per unit time. Vibrational data of bearings under varying operational environments are presented to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper focuses on the multi-choice tasks, which aim to select the correct choice for a given query by reasoning over texts, such as sentences and passages. Benefiting from the provided knowledge in these tasks, the reasoning of multi-choice models becomes well-founded. However, besides the evidence of the correct choice, the text usually contains some content related to other choices as well, which causes multi-choice models vulnerable to being confused by candidate choices, and we denote it as choice confusion problem. To alleviate this challenge, we propose two auxiliary mechanisms to distinguish the confusing choices. Specifically, a query-guided attention (QGA) mechanism is designed to automatically filter the text contents causing confusion by measuring the syntactic relevance between different contents and the query. Meanwhile, a confusion-aware training (CAT) mechanism is designed to learn the correct and easily confused choices asynchronously and perform a pushing-away operation between their selection processes. We conduct experiments on multi-choice tasks based on sentences and passages. The results show that our framework improves the choice selection accuracy compared to strong baselines. Furthermore, the ablation test and case study verify the effectiveness of our proposed QGA and CAT, especially in addressing the choice confusion problem. [ABSTRACT FROM AUTHOR]

Highlights • Novel approach to the cost-optimal early fault detection of CNC equipment. • VAR modeling of multivariate positioning data and residual testing for control purposes. • Estimation of the remaining useful life of CNC equipment using Bayesian approach. • Presented method outperforms previously published method based on HMM. Abstract The goal of a computer numerically controlled (CNC) equipment is to guarantee high specified performance and to maintain it effectively over its life cycle time. Thus, the assessment of health condition is crucial for industrial systems. However, very few papers have dealt with the cost-optimal early fault detection and remaining useful life prediction of CNC equipment using multivariate positioning data. The novel approach presented here is based on vector autoregressive (VAR) degradation modeling and hidden semi-Markov modeling using the optimal Bayesian control technique. System condition is modeled using a continuous time semi-Markov chain with three states, i.e. unobservable healthy state 1, unobservable warning state 2 and observable failure state 3. Model parameter estimates are calculated using the expectation-maximization (EM) algorithm. The optimal control policy for the three-state model is represented by a Bayesian control chart for a multivariate observation process. The optimization problem is formulated and solved in the semi-Markov decision process (SMDP) framework. A formula for the mean residual life (MRL) is also derived based on Bayesian approach, which enables the estimation of the remaining useful life and early maintenance planning based on the observed data. The validation of the proposed methodologies is carried out using actual multivariate degradation data obtained from a CNC equipment. A comparison with the multivariate Bayesian control chart based on a hidden Markov model (HMM) is given, which illustrates the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The computer numerical control (CNC) equipment is playing essential role in production and manufacturing, thus, its reliability estimation is crucial for industrial systems. Existing research studies mainly focused on univariate degradation data or multiple correlated data, which might not be accurate in equipment reliability assessment. In real CNC equipment, the degradation data consists of correlated and non-correlated degradation data and the data sample might be scarce, which bring great challenges for maintenance managers to assess risk and prevent failure ahead of fault occurrence. This paper proposes a reliability assessment approach based on multiple non-correlated and correlated degradation data with focusing on small size of data sample. Using the multiple degradation data of CNC equipment, a covariance matrix is built to determine the correlation between the performance signals. If the performance signals are not correlated, the degradation trajectory of the performance signals is curve fitted by least squares support vector machines (LS-SVM). The reliability which indicates the degradation curve not reaching its specified threshold is calculated. If the performance signals are correlated, according to the degradation data, the degradation curve of the performance signal is fitted by multivariate regression using LS-SVM, and the joint probability density function is derived. According to both the reliabilities of non-correlated degradation data and correlated degradation data, the joint reliability of the system is calculated. Finally, using a specific type of CNC machine tool as an example, the performance reliability assessment method is verified. [ABSTRACT FROM AUTHOR]

This paper considers an integrated framework for health measures prediction and optimal maintenance policy for mechanical systems subject to condition monitoring (CM) and random failure. We propose the proportional hazards model (PHM) to consider CM information as well as the age of the mechanical systems. Although the form of health prediction for the mechanical systems under periodic monitoring in the PHM with Markov chain was developed previously, the case of the continuous-state degradation process allowing possible degradation between the inspections still has not appeared. To this aim, the paper allows the use of Gamma process with non-constant degradation, which broadens the application area of PHM. A matrix-based approximation method is employed to compute health measures of the machine, such as condition reliability, mean residual life, residual life distribution. Based on the health measures, the optimal maintenance policy, which considers both hazard rate control limit and age control limit, is proposed and the optimization problem is formulated and solved in a semi-Markov decision process (SMDP) framework. The objective is to minimize the long-run expected average cost. The method is illustrated using two real data sets obtained from feed subsystem of a boring machine and GaAs lasers collected at regular time epochs, respectively. A comparison with other methods is given, which illustrates the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

The study works on a multi-level maintenance policy combining system level and unit level under soft and hard failure modes. The system experiences system-level preventive maintenance (SLPM) when the conditional reliability of entire system exceeds SLPM threshold, and also undergoes a two-level maintenance for each single unit, which is initiated when a single unit exceeds its preventive maintenance (PM) threshold, and the other is performed simultaneously the moment when any unit is going for maintenance. The units experience both periodic inspections and aperiodic inspections provided by failures of hard-type units. To model the practical situations, two types of economic dependence have been taken into account, which are set-up cost dependence and maintenance expertise dependence due to the same technology and tool/equipment can be utilised. The optimisation problem is formulated and solved in a semi-Markov decision process framework. The objective is to find the optimal system-level threshold and unit-level thresholds by minimising the long-run expected average cost per unit time. A formula for the mean residual life is derived for the proposed multi-level maintenance policy. The method is illustrated by a real case study of feed subsystem from a boring machine, and a comparison with other policies demonstrates the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

The study aims to work on multi -level condition-based maintenance (CBM) policy for multi-unit systems under economic dependence. Failure modes of units are classified as hard failure or soft failure and taken into account in this paper. Unlike previous works which only considered setup cost dependence with a single failure mode, two types of economic dependence are considered, which are setup cost-saving, and additional cost and time that can be saved when identical maintenance tasks and expertise are needed. In this study, hard failure provides an opportunity for additional inspection and repair of soft-type units, and soft failure creates an opportunity for replacement of the hard-type unit as its age exceeds opportunistic threshold. They both undergo preventive maintenance (PM) when the level/age exceeds preventive threshold at inspection epochs. The objective of this study is to determine the optimal opportunistic control limit and preventive control limit for each unit such that the system's average long-run maintenance cost per unit time is minimized. The optimization problem is formulated in a semi-Markov decision process (SMDP) framework. The explicit formula for the mean residual life is derived for the proposed model which enables the estimation of the remaining useful life and allows maintenance engineers to plan maintenance early based on the observed information. Finally, the method is illustrated by an industrial case study of a feed system of a machine tool. A comparison with other methods is given, which illustrates the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

• Development of an adaptive monitoring scheme for fault detection. • Multivariate hidden Markov model describes the deterioration process. • SMDP approach to formulate the control problem and to optimize the scheme. • Considerably better fault detection compared with published results. Limited by the installation of sensors and the structure of systems, only external observation information can generate an association with the internal health status of a system. In addition, modern industrial systems are often accompanied by hidden degradation processes, which present great challenges when monitoring and alerting impending failures. To address this issue, this paper proposes an adaptive monitoring scheme for predicting faults of systems with hidden degradation processes. A multivariate, continuous-time hidden Markov model with three states (hidden healthy state, hidden unhealthy state, and observable failure state) is established to describe the degradation process of the system. An expectation maximization (EM) algorithm is presented to estimate the parameters of the stochastic model. On the basis of the hidden degradation model, an adaptive Bayesian control scheme is developed for monitoring the potential risk of the system with two switchable sampling intervals. We calculate optimal decision variables of the control scheme to achieve the minimum expected average cost by a policy iteration algorithm under a semi-Markov decision process (SMDP). The performance and characteristics of the proposed scheme are demonstrated via real oil measurement data obtained from mechanical generators. A comparison case illustrates the superiority of the prediction performance of our monitoring scheme. [ABSTRACT FROM AUTHOR]

• Dynamic Bayesian control scheme for adaptive monitoring and detection. • Multivariate hidden semi-Markov model to describe the deterioration process. • SMDP approach to formulate and optimize the dynamic Bayesian scheme. • Considerably better fault detection compared with published results. • Development of case studies using multivariate data. Due to restrictions of mechanical structure and sensor installation, the actual status of modern engineered machine is unobservable and only partial observations can be indirectly collected. For this type of machine, existing research works used several measurable variables with fixed thresholds to monitor and detect the failures of the system, which leads to suboptimal results. This paper proposes a dynamic monitoring and detection approach for partially observed machines under discrete multivariate observations. The actual status of the partially observed machine system is modeled as a multivariate hidden semi-Markov process with unobservable operational states and an observable failure state under a general sojourn time structure. The process parameters are estimated using expectation maximization (EM) algorithm. Based on the dynamics of the hidden state of machine deterioration, a dynamic Bayesian control chart is formulated to monitor the posterior probability of system in warning state and adaptively switches the monitoring frequency according to risks of potential failures. As the posterior probability statistic exceeds a certain level, full inspection is initiated to detect the impending failures. The objective of the dynamic Bayesian control scheme is to achieve the maximum long-run system average availability, and the optimal control problem of monitoring and detection is formulated and solved by a computational algorithm in a semi-Markov decision process (SMDP) framework. The entire procedure is illustrated using multivariate data from case studies of mechanical generators and feed systems. Comparison with other advanced methods is also given, which demonstrates a considerably better performance of the proposed dynamic Bayesian approach. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to compare the per OPU clinical outcomes for transfer of Day 3 double cleavage-stage embryos (DET) and Day 5 single blastocyst-stage (SBT) in patients with five or fewer good quality embryos on day 3 per occyte pick-up cycle (OPU) in antagonist cycles with consideration of blastocyst formation failure. Methods: This was a retrospective, observational cohort study of 2,116 cases of OPU treated with antagonist protocol in the affiliated Chenggong Hospital of Xiamen University between January 2013 and December 2020. DET was performed in 1,811cycles and SBT was performed in 305 cycles. The DET group was matched to the SBT group by propensity score (PS) matching according to multiple maternal baseline covariates. After PS matching, there were 303 ET cycles in each group. The primary outcomes were the cumulative live birth rate (CLBR), cumulative multiple pregnancy rate(CMPR)per OPU and the number of ET to achieve live birth per OPU. Secondary outcomes were the percentage of clinical pregnancy(CPR), live birth rate(LBR), multiple pregnancy rate(MPR). Results: Following PS mating, the CLBR was slightly higher (48.8% versus 40.3% ; P = 0.041) and the CMPR was significantly higher in the DET group compared to SBT group(44.2% versus 7.9%, P < 0.001). The CPR, LBR and MPR per fresh transfer were higher in DET group compared to SBT group(50.2% versus 28.7%; 41.3% versus 21.5%;29.6% versus 0%, P < 0.001). The number of ET to achieve live birth per OPU in SBT group was obiviously more than in DET group(1.48 ± 0.578 versus 1.22 ± 0.557 ,P < 0.001). Conclusion: With a marginal difference cumulative live birth rate, the lower live birth rate per fresh transfer and higher number of ET per OPU in the SBT group suggested that it might take longer time to achieve a live birth with single blastocyst strategy. A trade-off decision should be made between efficiency and safety. [ABSTRACT FROM AUTHOR]

Maintenance improvement is affected by many uncertain factors such as maintainer experience, machine deterioration state, maintenance tools and equipment, it is very hard to determine each restoration degree ahead of repair operation. Therefore, this paper considers a maintenance problem in the presence of competing risks (degradation and sudden failure) under stochastic maintenance quality for a ship pump. The degradation process of ship pump is characterized by a non-homogeneous Gamma process and the hazard rate of sudden failure is described by a proportional hazards model. We propose a two-level condition-based maintenance (CBM) policy with stochastic maintenance quality. The policy combines degradation control limit and age control limit under stochastic maintenance quality of Beta distribution, normal distribution and uniform distribution, respectively, in maintenance decision-making. Maintenance optimization problem is formulated and solved by a computational algorithm in a semi-Markov decision process (SMDP) framework, with the objective of minimizing the long-run expected average cost. The proposed model is illustrated using a numerical study and a real case study of ship pump, respectively, and comparative results show the effectiveness and superiority of our approach. • Novel competing risks model incorporating two failure processes. • Development of three stochastic maintenance models. • Development of a two-level CBM policy. • Optimization of maintenance in a SMDP framework. • Comparisons of different stochastic CBMs via case studies. [ABSTRACT FROM AUTHOR]

• New, two-level Bayesian fault detection scheme is developed. • Realistic failure modeling considering two dependent failure modes. • Mean residual life formula is derived considering two failure modes in a new HMM framework. • The proposed two-level Bayesian control scheme outperforms previously published Bayesian control scheme. • Considerably lower average cost, reduced number of failures and more precise residual life estimates. A two-level Bayesian control approach is presented to detect early fault for mechanical equipment subject to dependent degradation and catastrophic failures. The system degradation process is modeled using a continuous time stochastic process with three states. To model the dependence of two failure modes, we assume that the joint distribution of the time to catastrophic failure and sojourn time in the healthy state follows Marshall-Olkin bivariate exponential distribution. To avoid unnecessary sampling cost and to effectively detect impending failure, a two-level control policy, where longer sampling interval is applied for healthier state and shorter sampling interval is used in severe degradation state is proposed in Bayesian control chart framework for a multivariate observation process considering dependent failure modes. The optimization problem is formulated and solved in the semi-Markov decision process (SMDP) framework. A formula for the mean residual life (MRL) is also derived using the Bayesian approach. The validation of the proposed methodologies is carried out using real multivariate degradation data obtained from a milling machine. A comparison with the multivariate Bayesian control chart with a single sampling interval and a single control limit is given, which illustrates the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Domain-adaptation technologies have been widely developed for mechanical fault diagnosis. Most related methods assume the same label space between the source and target domains, whereas the partial domain adaptation problem is more widespread and challenging. Moreover, most previous studies focused on data distribution alignment but failed to address fine-grained distributional differences of subdomains. Hence, this study proposes a novel duplex adversarial deep discriminative network (DADDN) for fault diagnosis in cross-domain partial transfer cases. First, a dual-domain adversarial attention mechanism is adopted in the DADDN to discriminate the label space and evaluate the transferability of the data samples. Second, a new metric function of the distribution difference is employed to learn both the fine-grained and discriminative features of the data samples and a weight-joint adaptive distribution mechanism is proposed to improve the domain confusion capability. Third, a center-of-balance weighting strategy is utilized to expand the categories of the target domain and reduce label space asymmetry. In addition, to avoid negative transfer, a weighting mechanism is designed to filter outlier categories, and a dynamic adaptive factor is introduced to weigh the importance of the marginal and conditional distributions. Finally, the effectiveness, robustness, and progressiveness of the proposed method are validated using several partial-transfer diagnostic tasks based on three rotating machinery datasets. [Display omitted] • A duplex domain adversarial attention module is given to improve model robustness. • A novel adaptive distribution mechanism is built to align the domain distribution. • A dynamic adaptive factor is given to balance inter-domain probability distribution. [ABSTRACT FROM AUTHOR]

Vehicle detection technology is of great significance for realizing automatic monitoring and AI-assisted driving systems. The state-of-the-art object detection method, namely, a class of YOLOv5, has often been used to detect vehicles. However, it suffers some challenges, such as a high computational load and undesirable detection rate. To address these issues, an improved lightweight YOLOv5 method is proposed for vehicle detection in this paper. In the presented method, C3Ghost and Ghost modules are introduced into the YOLOv5 neck network to reduce the floating-point operations (FLOPs) in the feature channel fusion process and enhance the feature expression performance. A convolutional block attention module (CBAM) is introduced to the YOLOv5 backbone network to select the information critical to the vehicle detection task and suppress uncritical information, thus improving the detection accuracy of the algorithm. Furthermore, CIoU_Loss is considered the bounding box regression loss function to accelerate the bounding box regression rate and improve the localization accuracy of the algorithm. To verify the performance of the proposed approach, we tested our model via two case studies, i.e., the PASCAL VOC dataset and MS COCO dataset. The results show that the detection precision of the proposed model increased 3.2%, the FLOPs decreased 15.24%, and the number of model parameters decreased 19.37% compared with those of the existing YOLOv5. Through case studies and comparisons, the effectiveness and superiority of the presented approach are demonstrated. • Novel YOLOv5 with a lightweight design for vehicle detection. • Convolutional block attention module improves model performance to select critical information. • CIoU_Loss function to speed up bounding box regression rate and improve localization accuracy. • Considerably better vehicle detection compared with related methods. • Demonstration of the improved performance using the two real cases. [ABSTRACT FROM AUTHOR]

• A novel scoring approach was first proposed for the motor function assessment of stroke survivors based on the WMFT-FAS scale. • The proposed approach mainly contained one Microsoft Kinect v2, one customized motion tracking system, and one customized intelligent scoring system. • The proposed approach can perform the whole WMFT-FAS scale with an accuracy of 0.924 ± 0.027. • The proposed multi-ReliefF method can dynamically select suitable features and increase the accuracy by around 2.9 %. Human-administered clinical scales, such as the Functional Ability Scale of the Wolf Motor Function Test (WMFT-FAS), are widely utilized to evaluate upper-limb motor function in stroke survivors. However, these scales are generally subjective and labor-intensive. To end this, we proposed a novel scoring approach for the motor function assessment. The proposed novel scoring approach mainly contained one Microsoft Kinect v2, one customized motion tracking system, and one customized intelligent scoring system. Specifically, the Kinect v2 was used to capture stroke survivors' functional movements, the motion tracking system was developed for recording the gathered movement data, and the intelligent scoring system (kernel: feed-forward neural network, FFNN) was developed to evaluate movement quality and provide corresponding WMFT-FAS scores. Several methods have been applied to enhance the approach's usability, such as singular spectrum analysis and multi-ReliefF method. Sixteen stroke survivors and ten healthy subjects were recruited for validation. Inspiring results of the proposed approach were achieved when compared with the clinical scores provided by a physiotherapist: 0.924 ± 0.027 for accuracy, 0.875 ± 0.063 for F1-score, 0.915 ± 0.051 for sensitivity, 0.969 ± 0.013 for specificity, 0.952 ± 0.038 for AUC, 0.098 ± 0.037 for mean absolute error, and 0.214 ± 0.078 for root mean squared error. The results indicate that the proposed novel scoring approach can provide objective and accurate assessment scores, which can help physiotherapists make individualized treatment decisions. [Display omitted] [ABSTRACT FROM AUTHOR]

• A new two-stage feature-fusion method by combining PCA and KLPP is presented. • A unique approach for determining the model parameters of KLPP is presented. • KLPP is comprehensively analyzed under three weighting methods. • KLPP helps to improve the prediction accuracy and compress the CI of Standard_SBLR. • Under the support of KLPP, Standard_SBLR shows superior predictive performance. To further improve the prediction accuracy of surface roughness in milling process, this paper presents a new two-stage feature-fusion method by combining principal component analysis (PCA) and kernel locality preserving projection (KLPP). PCA is utilized for dimension-reduction while KLPP is utilized for dimension-increment. Vibration information of the workpiece, fixture and spindle is adopted as the monitoring signal. Firstly, the commonly-used time-domain features are extracted from the vibration signals. Then, the presented two-stage feature-fusion method is carried out for extracting more effective signal features. Besides, two types of Bayesian linear regression (BLR) model (Standard_BLR and Standard_SBLR) are utilized for model construction. Before the two-stage feature-fusion, Standard_BLR is utilized to determine the optimum dimension of PCA-based fusion features and the model parameters of KLPP. After the two-stage feature-fusion, Standard_SBLR is utilized to construct the BLR-based surface roughness predictive model. Two types of milling experiment (down milling and up milling) are carried out to show the influence of the presented two-stage feature-fusion method on the predictive performance of Standard_SBLR. Experimental results show that KLPP is highly effective in improving the prediction accuracy and compressing the confidence interval (CI) of Standard_SBLR. Moreover, the comparison results show that the effectiveness of KLPP is not inferior to kernel principal component analysis (KPCA). This paper lays the foundation for accurate monitoring of surface roughness in real industrial settings. [ABSTRACT FROM AUTHOR]

Unmanned underwater vehicles (UUVs) are invaluable tools for ensuring the safety and efficiency of marine activities and have shown exceptional potential in resource exploration, underwater facility construction, and seafood fishing. However, unpredictable and complex underwater environments pose significant risks to UUVs, making them susceptible to faults that threaten their motion reliability and system safety. Fault-tolerant control (FTC) techniques have been developed to improve the system robustness and provide acceptable performance in the presence of faults, thereby enhancing their reliability and safety. This study conducts a comprehensive analysis and provides a summary of the latest UUV-FTC research spanning 1999–2022, covering actuators, sensors, and cooperative systems. FTC for actuating systems is classified into multi-model reconfiguration, actuator thrust reconfiguration, and thrust reallocation. Additionally, this study explores fault-tolerant strategies (FTSs) for sensors that leverage physical or analytical redundancies to reproduce or correct sensor signals. Furthermore, the study addresses FTCs for multi-UUV cooperation systems, focusing primarily on issues related to consensus, containment, and formation under actuator or communication-link faults. Finally, the unresolved scientific issues are discussed to inspire future research. The UUV-FTC review presented a complementary perspective by revealing both the shared characteristics and unique aspects, which have not been addressed in existing FTC reviews. • Papers about UUV FTC are reviewed concerning actuator, sensor, and multi-UUV communication faults. • FTC schemes facing the UUV actuator faults are divided into multi-model and thrust reconfiguring categories. • Schemes for tolerating UUV sensor faults are categorized as alternative and corrective strategies. • FTC schemes toward multi-UUV cooperation are separated based on the faults of actuators and communication links. • FTC issues related to nonlinearity, rapidity, synthesis, free of models, intelligence, and applications are summarized. [ABSTRACT FROM AUTHOR]

• A method of extracting effective features for monitoring surface roughness is presented. • KPCA_IRBF is re-derivated according to the derivation of PCA. • A new kind of BLR model abbreviated as Standard_SBLR is firstly proposed. • KPCA_IRBF helps to improve the prediction accuracy and ameliorate the CI of BLR. • Under the support of KPCA_IRBF, Standard_SBLR shows superior predictive performance. To improve the prediction accuracy of surface roughness in milling process, this paper provides an unique feature extraction method and comprehensively analyzes four types of Bayesian linear regression (BLR) model (Standard_BLR, Gaussian_BLR, Standard_SBLR and Gaussian_SBLR). Among them, Standard_SBLR is firstly proposed. Vibration information of the workpiece, fixture and spindle is adopted as the monitoring signal. The unique feature extraction method consists of three stages: extraction of time-domain features from the vibration signals, dimension-reduction by principal component analysis (PCA) and dimension-increment by the integrated radial basis function based kernel principal component analysis (KPCA_IRBF). The BLR models can provide both the predicted value and the corresponding confidence interval (CI). Two types of milling experiment (down milling and up milling) are conducted to reveal the influence of dimension-increment process of KPCA_IRBF on the predictive performance of the BLR models. Experimental results show that when combined with KPCA_IRBF, Standard_SBLR has the best predictive performance among the four BLR models. This also shows that KPCA_IRBF is highly effective in improving the prediction accuracy and compressing the CI of Standard_SBLR. To further prove the superiority of Standard_SBLR, other powerful machine learning methods such as partial least squares regression (PLS), artificial neural network (ANN) and support vector machine (SVM) are also utilized to realize surface roughness prediction under the support of KPCA_IRBF. This paper lays the foundation for accurate monitoring of surface roughness in real industrial settings. [ABSTRACT FROM AUTHOR]

Accurate identification of the tool wear states in machining of titanium alloys continues to be a thorny problem. Rapid construction of accurate and effective tool wear predictive models with regard to various titanium alloys is indispensable for promoting the development of intelligent manufacturing. This article presents a novel WOA-SVM model that integrates support vector machine (SVM) and whale optimization algorithm (WOA), which is utilized for accurate estimation of the tool wear in end milling of titanium alloy Ti-6Al-4V under variable cutting conditions. The signal features in three domains are extracted from the original cutting forces and constitute the “complete features” that are adopted as the monitoring features. Besides, neighborhood preserving embedding (NPE) is utilized to fuse the monitoring features and realize dimension reduction, which aims at improving the modeling efficiency of the novel WOA-SVM model. The experimental results show that under the premise of guaranteeing prediction accuracy, the utilization of NPE reduces the modeling time-consumption of WOA-SVM by more than 90%. In comparison with the commonly used methods (such as PSO-SVM and GSA-SVM), WOA-SVM takes on comparable prediction accuracy and can reduce the modeling time-consumption by more than 30% in most cases. Moreover, the WOA-SVM model has better prediction accuracy and stability than other classical methods, such as ${k}$ -nearest neighbor ($k$ -NN), feedforward neural network (FFNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and classification and regression tree (CART). Therefore, the combination of NPE and WOA-SVM provides a guarantee for rapidly constructing accurate tool wear predictive models in machining of titanium alloys. [ABSTRACT FROM AUTHOR]

• A novel IRBF-RVM model is proposed for the diagnosis of atrial fibrillation (AF). • The kernel parameter of IRBF-RVM has a much larger selectable region than RBF-RVM. • IRBF-RVM performs better than SVM in terms of rapid modeling and sparseness. • IRBF-RVM outperforms k -NN, Naive Bayes, FFNN, AdaBoost and RF, in terms of prediction accuracy. • The generalization performance of IRBF-RVM is not inferior to other powerful machine learning methods. Atrial fibrillation (AF) is one of the common cardiovascular diseases, and electrocardiography (ECG) is a key indicator for the detection and diagnosis of AF and other heart diseases. In this study, an improved machine learning method is proposed for rapid modeling and accurate diagnosis of AF. This paper presents a novel IRBF-RVM model that combines the integrated radial basis function (IRBF) and relevance vector machine (RVM), which is utilized for the diagnosis of AF. The synchronous 12-lead ECG signals are collected from the human body surface so as to fully reflect the electrical activity of the whole heart. RR intervals of the QRS-waves in ECG signals are obtained by means of the classical Pan–Tompkins algorithm. The RR-features extracted from RR intervals are adopted as the diagnostic features for AF patients. In addition, the conventional RBF-RVM model, support vector machine (SVM) and other machine learning methods are also investigated for the diagnosis of AF so as to reflect the advantage of the proposed IRBF-RVM model. The open MIT-BIH arrhythmia database (MITDB) is also used to evaluate the predictive performance of these state-of-the-art methods. Altogether 1056 AF patients and 904 healthy people are participated in this study and validate the effectiveness of each channel of the 12-lead ECG signals. Experimental results show that the classification rate of IRBF-RVM can reach up to 98.16% by recurring to Channel II of the 12-lead ECG signals. IRBF-RVM absorbs the advantages of IRBF, which makes the kernel parameter of IRBF-RVM have a much larger selectable region than RBF-RVM. In addition, RVM has faster modeling and recognition speed in comparison with SVM. This work lays the foundation for the application of RVM to accurate diagnosis of AF. [ABSTRACT FROM AUTHOR]

• A novel tool wear predictive model based on KPCA_IRBF and RVM is developed. • KPCA_IRBF helps to improve the prediction accuracy of RVM. • KPCA_IRBF helps to ameliorate the confidence interval (CI) of RVM. • CI is an extra advantage over the traditional methods such as PLS, ANN and SVM. • In comparison with SVM, RVM has high sparseness and comparable prediction accuracy. In order to realize real-time and accurate monitoring of the tool wear in machining process, this paper presents a tool wear predictive model based on the integrated radial basis function based kernel principal component analysis (KPCA_IRBF) and relevance vector machine (RVM). The traditional methods such as partial least squares regression (PLS), artificial neural network (ANN) and support vector machine (SVM) can only provide predicted values which have no probabilistic significance. As a sparse probabilistic model, RVM can provide both the predicted value and the corresponding confidence interval (CI). However, the existence of process noises and redundancy will seriously affect the prediction accuracy and the stability of CI. As a new dimension-increment technique, KPCA_IRBF helps to weaken the negative effects of process noises and redundancy by increasing the dimensionality of monitoring features. The fused features obtained by KPCA_IRBF are more sensitive to the change of tool wear. Two different cutting experiments are carried out to verify the effectiveness of KPCA_IRBF in improving the prediction accuracy and ameliorating the CI of RVM. The experimental results show that KPCA_IRBF can reduce the root mean square error (RMSE) of RVM by more than 30% and compress the average width of CI by more than 90%. To further show the advantages of RVM, the traditional methods such as PLS, ANN and SVM are also utilized to realize tool wear prediction. This paper lays the foundation for the application of RVM to industrial field. [ABSTRACT FROM AUTHOR]

Most maintenance optimization models of gear systems have considered single failure mode. There have been very few papers dealing with multiple failure modes, considering mostly independent failure modes. In this paper, we present an optimal Bayesian control scheme for early fault detection of the gear system with dependent competing risks. The system failures include degradation failure and catastrophic failure. A three‐state continuous‐time–homogeneous hidden Markov model (HMM), namely the model with unobservable healthy and unhealthy states, and an observable failure state, describes the deterioration process of the gear system. The condition monitoring information as well as the age of the system are considered in the proposed optimal Bayesian maintenance policy. The objective is to maximize the long‐run expected average system availability per unit time. The maintenance optimization model is formulated and solved in a semi‐Markov decision process (SMDP) framework. The posterior probability that the system is in the warning state is used for the residual life estimation and Bayesian control chart development. The prediction results show that the mean residual lives obtained in this paper are much closer to the actual values than previously published results. A comparison with the Bayesian control chart based on the previously published HMM and the age‐based replacement policy is given to illustrate the superiority of the proposed approach. The results demonstrate that the Bayesian control scheme with two dependent failure modes can detect the gear fault earlier and improve the availability of the system. [ABSTRACT FROM AUTHOR]

• The proposed fall prediction paradigm got promising results (0.933–0.950 accuracy). • The customized multi-ReliefF method enhanced the accuracy (up to 3.1% improvement). • The proposed paradigm can be easily conducted during routine physical exercises. This study aimed to predict the fall risk of community-dwelling seniors through fitness screening protocols and machine learning algorithms. Specifically, a customized questionnaire, simple functional performance tests (six items), and physical fitness tests (six items) were employed. A data processing was conducted to enhance the performance, including a deep neural network-based imputation approach (DataWig), an adaptive index filtration algorithm (multi-ReliefF), and decision tree (DT)-based prediction models. Historical data contained 215 community-dwelling seniors were used, and results showed that the proposed paradigm achieved promising and stable performance (e.g., accuracy: 0.933–0.950, F1-score: 0.951–0.955). The developed DT models presented satisfactory performance (top three in almost all evaluation metrics), and the utilized multi-ReliefF enhanced the accuracy (from 1.0% to 3.1%). These results indicated that the proposed paradigm could be a helpful tool for monitoring the health conditions of seniors during their daily exercises, which could help reduce the risk of falls. [ABSTRACT FROM AUTHOR]

The ocean surroundings are mostly complex and harsh, which make the faults of the unmanned underwater vehicles (UUVs) difficult to avoid. Fault diagnosis (FD) technologies for UUVs can assist in providing the damage states and are helpful for implementing the targeted fault-tolerant strategies with which further expansion of the losses could be prevented. In the past three decades, a lot of researches on UUV FD have been carried out, from which the findings are classified and analyzed in this paper to present a clear status of the development for the future studies. The task, capability, and component layers of FD are defined based on the specific control structure of the UUVs, where the task layer FD concerns the overall risks of the system operation and task execution, the capability layer FD describes the comprehensive effects on the motion and sensing features with multiple components, and the component layer FD pays attention to the physical information toward specific components. For each layer, the literatures are organized and briefly explained based on the similarities of solutions. Accordingly, some worthwhile issues to be solved are summarized. • The FD literatures of UUVs are reviewed in layers of task, capability, and component concerning the solution similarities. • The overall risks of the system operation and task execution are concerned in the task layer FD. • The comprehensive effects on the motion and sensing characteristics of the components are analyzed in the capability layer. • The comparison-based FD concerning the physical information is contained in the component layer. • Worthwhile issues concerning the adaptive fault threshold, active FD, multi-layer FD, and FD applications are summarized. [ABSTRACT FROM AUTHOR]

To ensure the mission implementation of Autonomous Underwater Vehicles (AUVs), faults occurring on actuators should be detected and located promptly; therefore, reliable control strategies and inputs can be effectively provided. In this paper, faults occurring on the propulsion and attitude control systems of a torpedo-shaped AUV are analyzed and located while fault features may induce confusions for conventional fault localization (FL). Selective features of defined fault parameters are assorted as necessary conditions against different faulty actuators and synthesized in a fault tree subsequently to state the sufficiency towards possible abnormal parts. By matching fault features with those of estimated fault parameters, suspected faulty sections are located. Thereafter, active FL strategies that analyze the related fault parameters after executing purposive actuator control are proposed to provide precise fault location. Moreover, the generality of the proposed methods is analyzed to support extensive implementations. Simulations based on finite element analysis against a torpedo-shaped AUV with actuator faults are carried out to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]