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The multi-subpopulation refracted adaptive salp swarm algorithm (MRASSA) was proposed for vibration control in 1/4 semi-active suspension systems. The MRASSA algorithm was applied to optimize suspension damping performance by addressing the local optimal and slow convergence speed challenge of the standard salp swarm algorithm for two-degrees-of-freedom 1/4 semi-active suspension systems. The developed MRASSA contains three key improvements: (1) partitioning multi-subpopulation; (2) applying refracted opposition-based learning; (3) adopting adaptive factors. In order to verify the performance of the MRASSA approach, a 1/4 suspension Simulink model was developed for simulation experiments. To further validate the results, a physical platform was built to test the applicability of the simulation model. The optimized suspension performance of MRASSA was also compared with three optimized models, namely, standard SSA, Single-Objective Firefly (SOFA) and Whale-optimized Fuzzy-fractional Order (WOAFFO). The experimental results showed that MRASSA outperformed the other models, achieving better suspension performance in complex environments such as a random road with a speed of 60 km/h. Compared to passive suspension, MRASSA led to a 41.15% reduction in sprung mass acceleration and a 15–25% reduction compared to other models. Additionally, MRASSA had a maximum 20% reduction in suspension dynamic deflection and dynamic load. MRASSA also demonstrated a faster convergence speed, finding the optimal solution faster than the other algorithms. These results indicate that MRASSA is superior to other models and has potential as a valuable tool for suspension performance optimization. [ABSTRACT FROM AUTHOR]

Many systems are required to perform a series of missions with finite breaks between any two consecutive missions. To improve the probability of system successfully completing the next mission, maintenance action is carried out on components during the breaks. In this work, a selective maintenance model with stochastic maintenance quality for multi-component systems is investigated. At each scheduled break, a set of maintenance actions with different degrees of impact are available for each component. The impact of a maintenance action is assumed to be random and follow an identified probability distribution. The corresponding maintenance cost and time are modelled based on the expected impact of the maintenance action. The objective of selective maintenance scheduling is to find the cost-optimal maintenance action for each component at every scheduled break subject to reliability and duration constraints. A simulated annealing algorithm is used to solve the complicated optimisation problem where both multiple maintenance actions and stochastic quality model are taken into account. Two illustrative numerical examples and a real case study have been solved to demonstrate the performance of the proposed approach. A comparison with deterministic maintenance shows the importance of considering the proposed stochastic quality in selective maintenance scheduling. [ABSTRACT FROM AUTHOR]

Lubricating oil, which carries information about machine's health condition, is of great importance to the performance of machines in the full life cycle. The main purpose of oil deterioration modeling and its remaining useful life prediction is to determine the exact time that the lubricating oil has degraded and it is no longer able to maintain its functions. Generally, lubricating oil deterioration can be partially detected by condition monitoring based on wear debris analysis, and thus can be categorized into three states. In our paper, vector data, which contain wear debris concentration and carry information about the state of lubricating oil, are obtained by an on-line visual ferrograph sensor from a four-ball tester at regular sampling epochs. The oil's state process is described by a hidden semi-Markov model, and its sojourn times in each state are assumed to be Erlang distributed. A vector autoregressive method based on time series modeling is presented to obtain residual observations, which are regarded as the observable process of oil information in the hidden semi-Markov model framework. The unknown parameters of the hidden semi-Markov model are then estimated by using expectation-maximization algorithm. Afterward, a Bayesian updating approach is presented to derive the explicit formulas of the conditional reliability and mean residual life. To validate the proposed approach, a real case study of lubricating oil deterioration is demonstrated and a comparison with the hidden Markov model is given to illustrate the effectiveness of the new developed remaining useful life prediction approach for lubricating oil. [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]

Modern engineering components generally work under aging and dynamic cumulative damage processes. To prevent failures of such components, the proportional hazards model (PHM) was proposed to integrate both processes for health prognostics. However, the existing PHMs use constant damage rate within monitoring intervals for machine health estimation and still lack consideration of dynamic operational conditions, which fails to model the practical degradation situations. This article presents a prognostic model using a new PHM to consider aging and environment-varying cumulative damage for engineering machines. A dynamic multistate process with practical transition mechanisms under varying operational conditions is presented to model the cumulative damage progress. To address the difficulties in prognostics with PHMs, a matrix-based approximation method with low computational load is developed to compute important health measures such as conditional reliability, mean residual life (MRL) and residual life distribution. A prognostic scheme featuring online prediction and dynamic updating is presented. The particularity of the proposed model is that it considers dynamic environments and can be applied to a large number of deteriorating states. The proposed approach is illustrated using a case of pump under different operating environments, and comparison with other advanced PHM is given to validate the applicability and effectiveness of the proposed approach. [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]

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 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]

• 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]

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]

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]