Failure mode and effects analysis (FMEA) is a widely used reliability analysis tool for identifying and eliminating known or potential failures in system, design, and process. In traditional FMEA, failure modes are evaluated by FMEA team members with respect to three risk factors: severity (S), occurrence (O), and detectability (D), and ranked via their risk priority number (RPN), which is obtained by multiplying the crisp values of S, O, and D. However, traditional RPN has been considerably criticized due to the following shortcomings: not considering the different weights of risk factors; the identical value of RPN for different combinations of S, O, and D; the diversity and uncertainty of evaluation information given by FMEA team members and without considering the dependence among different failure modes. Although significant efforts have been made in FMEA literatures to overcome these shortcomings, there are still some deficiencies. In this paper, a new risk priority model is presented for FMEA by using the house of reliability (HoR)-based rough VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) approach. In the proposed model, the HoR is introduced to identify the dependence among different failure modes and the link between failure modes and the risk factors of O, D and the subcriteria of S. Rough number is introduced to manipulate the subjectivity and vagueness in decision making and VIKOR approach is used to determine the risk priority order of failure modes in a comprehensive way. Finally, an illustrative case in transmission system of a vertical machining center has demonstrated the effectiveness and practicality of the proposed model.

The assembly quality of a complex product is the result of the combined effects of multiple manufacturing stages, including design, machining and assembly, and it is influenced by associated elements with complex coupling mechanisms. These elements generate and transmit assembly quality deviations during the assembly process which are difficult to analyze and express effectively. Current studies have focused on the analysis and optimization of the assembly surface errors of single or few components, while lacking attention to the impact of errors on the whole product. Therefore, in order to solve the above problem, an assembly quality deviation analysis (AQDA) model is constructed in this paper to analyze the deviation transfer process in the assembly process of complex products and to obtain the key features to optimize. Firstly, the assembly process information is extracted and the assembly quality network model is established on the basis of complex networks. Second, the Jacobian-Torsor (J-T) model is introduced to form a network edge weighting method suitable for the assembly process to objectively express the error propagation among product part features. Third, an error propagation model (EPM) is designed to simulate the error propagation and diffusion processes in the assembly network. Finally, the assembly process of an aero-engine fan rotor is used as an example for modeling and analysis. The results show that the proposed method can effectively identify the key assembly features in the assembly process of complex products and determine the key quality optimization points and monitoring points of the products, which can provide a decision basis for product quality optimization and control.

Aiming at the optimization problem of multiple objectives with contradictions and conflicts in the process of allocating the tolerance for complex products, taking advantage of the features of coordinating, and balancing contradictions and conflicts of cooperative game theory, this paper uses cooperative game theory to solve the multiobjective optimization problem of tolerance allocation. The quality requirements and cost requirements of assembly products are used as the game decision parties, and the fuzzy clustering method is used to group the design variables of tolerance allocation problem of the steam turbine to form the strategic space of game parties. Take the quality level and cost level of the assembly product as the optimization goals, complete the calculation of the utilities of the two game parties, and establish the multiobjective optimization model of tolerance allocation based on cooperative game theory. Finally, the Shapley value method based on cooperative game theory, the Nash equilibrium method based on noncooperative game theory, and the traditional single‐objective optimization method with the quality as the constraint and the cost as the optimization objective are used to solve the tolerance allocation problem of steam turbine. The solution results show that the method of cooperative game realized the balance, coordination, and comprehensive optimization of the quality and cost from the perspective of collective interests, overcame the shortcomings of the traditional single‐objective optimization method, and obtained better result than the Nash equilibrium method.

As an effective alternative to the power cables and overhead lines in the electrical transmission networks, the gas-insulated metal enclosed transmission line (GIL) has been receiving ever-increasing usage worldwide. The GIL reliability, as one of the important characteristics of GIL equipment, is greatly determined by the processing technologies used in manufacturing, transportation, and installation stages, and failures caused by poor processing technologies can cause great damage to GIL equipment, which can further result in great losses to the electrified wire netting. Thus, it is necessary to study and accurately predict the reliability of GIL equipment to discover potential failure modes and take precautions before GIL equipment goes into operation. In this paper, a new reliability prediction model called the multilayer directed and weighted network (M-LDAWN) reliability prediction model is proposed to analyze and predict the GIL equipment reliability by concerning failure propagation and practical processing technologies. In the reliability prediction process, the reliability minimum cut sets triggered by substandard processing technologies are searched by the M-LDAWN model, and the increased failure rate of GIL equipment under each reliability minimum cut set is calculated considering the amplification and concatenation effects of failure propagation. Using the proposed reliability prediction model, the effects of substandard processing technologies on the GIL equipment reliability can be quantified, and the reliability fluctuation caused by them can be calculated. The effectiveness of the proposed framework is demonstrated on a practical GIL reliability prediction example.

Fault diagnosis methods based on deep learning have made certain progress in recent years. However, in actual industrial scenarios, there are severe strong background noise and limited computing resources, which poses challenges to the practical application of fault diagnosis models. In response to the above issues, this paper proposes a novel noise-resistant and lightweight fault diagnosis framework with nonlinear timestep degenerative greedy strategy (NTDGS) and dual residual horizontal feature pyramid (DRHFPN) for fault diagnosis in strong noise scenarios. This method takes advantage of the strong fitting ability of deep learning methods to model the agent in reinforcement learning by the ways of parameterization, fully leveraging the advantages of both deep learning and reinforcement learning methods. NTDGS is further developed to adaptively adjust the action sampling strategy of the agent at different training stages, improving the convergence speed of the network. To enhance the noise resistance of the network, DRHFPN is constructed, which can filter out interference noise at the feature map level by fusing local feature details and global semantic information. Furthermore, the feature map weighting attention mechanism (FMWAM) is designed to enhance the weak feature extraction ability of the network through adaptive weighting of the feature maps. Finally, the performance of the proposed method is evaluated in different datasets and strong noise environments. Experiments show that in various fault diagnosis scenarios, the proposed method has better noise resistance, higher fault diagnosis accuracy, and fewer parameters compared to other methods.

Aiming at the problem of low resources utilization of rotating blades in the selection process of aeroengine fan rotor blades, this paper takes the first‐order bending dispersion, first‐order torque dispersion, and gravitational moment difference of rotor blades as the selection criteria and takes the minimum remaining blades as the optimization goal. An intelligent selection algorithm of blades based on the collocation degree of blades is proposed and achieves the efficient selection and full utilization of rotating blades. Aiming at the problem of multiple installations and multiple adjustments and low assembly success rate of fan rotor blades, this paper takes the gravity moment difference of the two blades at the diagonal position of 180° as the constraint and takes the minimum residual unbalance as the optimization objective, adopts the improved simulated annealing algorithm to optimize the assembly sequence of rotating blades, and greatly reduces the residual unbalance of blades, which is beneficial to reduce the number of assembly adjustments of blades. The optimizing selection and optimizing matching methods of rotating blades realize the full utilization and efficient assembly of blades and lays a foundation for the reliability and robustness of the assembly quality and service performance of blades.