Hydraulic systems are widely employed across diverse industrial production processes. Nevertheless, complexity of their system structure presents challenges in developing a maintenance strategy. This paper develops a maintenance strategy for hydraulic systems by proposing an integrated approach that takes into account epistemic uncertainty and multi-source information. Initially, a hydraulic system is modeled using a fault tree, which is subsequently converted into a generalized stochastic Petri net model. A Monte Carlo simulation algorithm is proposed to deal with the epistemic uncertainty that arises from the interval-value failure rates of basic events within its complex structure. As a result, importance measures are calculated for each component. Next, experts are invited to evaluate maintenance cost of components, and their evaluation results are aggregated. Moreover, importance measures and maintenance cost are used to construct an original decision table, and an improved combinative distance-based assessment method is developed to obtain the maintenance strategy for the system. Finally, a case study is conducted on a hydraulic system of a tipping truck with side-pressing mechanism to demonstrate the generality of the proposed methodology.

Due to the lack of faulty data on the target machine, intelligent networks often need to learn fault knowledge from other relevant machines. Unfortunately, data from different machines introduce individualized deviations, which may lead to overfitting of network learning and reduce its generalization ability. To address the problem, this article proposes a collaborative multimachine generalization method—causal consistency network (CCN), which mines the invariant causal information in individualized machines through a collaborative way to achieve knowledge generalization. In CCN, instead of emphasizing the domain invariance of features, causal consistency loss depicting the consistency of fault causality representations in deep latent variables is proposed. Moreover, to transform the individualized data of different machines into consistent representations, a collaborative training loss is proposed to describe the underlying invariant causal mechanism of the fault features. The generalization results among 6 machines containing 43 individual bearings and 20 operating conditions demonstrate the superiority of CCN.

Purpose This paper aims to solve the major assessment problem in matching the satisfaction of psychological gratification and mission accomplishment pertaining to volunteers with the disaster rescue and recovery tasks. Design/methodology/approach An extended belief rule-based (EBRB) method is applied with the method's input and output parameters classified based on expert knowledge and data from literature. These parameters include volunteer self-satisfaction, experience, peer-recognition, and cooperation. First, the model parameters are set; then, the parameters are optimized through data envelopment analysis (DEA) and differential evolution (DE) algorithm. Finally, a numerical mountain rescue example and comparative analysis between with-DEA and without-DEA are presented to demonstrate the efficiency of the proposed method. The proposed model is suitable for a two-way matching evaluation between rescue tasks and volunteers. Findings Disasters are unexpected events in which emergency rescue is crucial to human survival. When a disaster occurs, volunteers provide crucial assistance to official rescue teams. This paper finds that decision-makers have a better understanding of two-sided match objects through bilateral feedback over time. With the changing of the matching preference information between rescue tasks and volunteers, the satisfaction of volunteer's psychological gratification and mission accomplishment are also constantly changing. Therefore, considering matching preference information and satisfaction at two-sided match objects simultaneously is necessary to get reasonable target values of matching results for rescue tasks and volunteers. Originality/value Based on the authors' novel EBRB method, a matching assessment model is constructed, with two-sided matching of volunteers to rescue tasks. This method will provide matching suggestions in the field of emergency dispatch and contribute to the assessment of emergency plans around the world.

Heterogeneous information fusion has long been a difficult problem due to the differences in the representation and feature of various physical information. Besides, the multisensor signals of large mechanical equipment, such as aerospace engines, often change in a complicated way during the start-up stage and long-term operation, which makes the multisensor fusion-based health assessment research impending. To explore a suitable fusion method for multiphysical signals with different change rates and to monitor the health state of large mechanical equipment based on multisensor information, this article proposes a heterogeneous time-tracking fusion algorithm. First, the time-domain indexes and instantaneous frequencies of the fast-varying harmonic-like signals are obtained by employing index extraction and second-order synchrosqueezing transform, respectively, by which the overall and detailed characteristics of the signals are thus obtained. Second, after structuring a dynamic time-tracking function consisting of the hyperbolic tangent function and modified arctangent function, the time-dynamic confidence upper limit for fast-varying signals and the confidence interval for slow-varying signals are obtained creatively. Finally, the different varying-rate signals are fused into a dynamic normalized time-varying index representing the health state through the aforementioned functions. By applying the proposed method to the health evaluation for ignition start-up stage of gas generators and the long-term performance of the turbopump, its effectiveness and practicability in the aerospace engine health analysis have been validated.

Application of new technology in modern systems not only substantially improves the performance, but also presents a severe challenge to fault location of these systems. This paper presents a new fault location strategy for maintenance personnel to recover them based on information fusion and improved CODAS algorithm. Firstly, a fault tree is adopted to develop the failure model of a complex system, and failure probability of components is determined by expert evaluations to handle the uncertainty problem. Moreover, a fault tree is converted into an evidence network to obtain importance degrees, which are used to construct a diagnostic decision table together with the risk priority number. Additionally, these results are updated to optimize the maintenance process using sensor information. A novel dynamic location strategy is designed based on interval CODAS algorithm and optimal fault location strategy can be obtained. Finally, a real system is analyzed to demonstrate the feasibility of the proposed maintenance strategy

Owing to expensive cost and restricted structure, limited sensors are allowed to install in modern systems to monitor the working state, which can improve their availability. Therefore, an effective sensor placement method is presented based on a VIKOR algorithm considering common cause failure (CCF) under epistemic uncertainty in this paper. Specifically, a dynamic fault tree (DFT) is developed to build a fault model to simulate dynamic fault behaviors and some reliability indices are calculated using a dynamic evidence network (DEN). Furthermore, a VIKOR method is proposed to choose the possible sensor locations based on these indices. Besides, a sensor model is introduced by using a priority AND gate (PAND) to describe the failure sequence between a sensor and a component. All placement schemes can be enumerated when the number of sensors is given, and the largest system reliability is the best alternative among the placement schemes. Finally, a case study shows that CCF has some influence on sensor placement and cannot be neglected in the reliabilitybased sensor placement.