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Failure mode and effects analysis is a widely applied risk assessment method in various engineering and management domains. However, the identification of failure modes is difficult and uncountable. Therefore, a function-motion-action (FMA) decomposition method is developed to identify failure modes from the perspective of motion and extraordinarily suitable for mechatronic products. In the typical risk assessment, the ranking orders of failure modes are determined by risk priority number (RPN), which has been criticized for several drawbacks and improved by some alternative RPNs, but some drawbacks still exist, such as duplicate values, narrow admissible value range, and missing failure modes' and risk factors' weights. This study formulates several alternative weighted RPNs to overcome the above drawbacks, and the final ranking orders of failure modes are garnered through the integrated RPN (IRPN). First, failure modes are identified via the proposed FMA decomposition method and evaluated with crisp values, whose weights are aggregated from the basic failure modes' weights. Second, the weights of the basic failure modes, risk factors and different RPN methods are derived from analytic hierarchy process. Third, the conditional weights of risk factors are determined by incorporating risk factors' weights and failure modes' conditional weights deduced from Shannon entropy. Next, several alternative weighted RPNs and IRPN are formulated to rank failure modes' risk levels. Finally, an illustrative example about computer numerical control machine center is presented to demonstrate the application and effectiveness of the proposed method.

The assisted driving system of combine harvester with the mid-angle error carries the path-tracking deviation and limits the harvesting efficiency. In the manual calibration method, the accuracy and convenience of mid-angle calibration are limited by the unreliable driving skills. This paper analyzes the trajectory of harvester and builds the error model of mid-angle. An improved path-tracking controller with mid-angle adaptive calibration is proposed. The new controller calculates the average lateral errors of harvester through an interval window adjusted by fuzzy controller. The mid-angle is calibrated and the path-tracking deviation is restrained by the increment PID model. To improve the calibration accuracy and convergence speed, the PID parameters are optimized by particle swarm optimization (PSO). However, the inertia weight of adaptive inertia weight PSO decreases too fast in path-tracking system, a high-order system. The improved PSO, proposed in this paper, combines the iteration number and feedback function as the inertia weight strategy and adapts inertia weight. The experiments demonstrate that the new controller can restrain the path-tracking deviation from 17.02 cm to −0.73 cm and improve the accuracy of harvesting. K: Data processing methods; Pattern recognition, cluster finding, calibration and fitting methods 1Corresponding author.

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In order to enhance quality and reliability of mechanical and electrical products, the methods of taking corresponding corrective measures to eliminate or alleviate product failure in advance have been widely concerned. Failure mode and effects analysis (FMEA) is a typical prevention reliability analysis method. However, there are some drawbacks in traditional FMEA method. To overcome these drawbacks, we propose a hybrid risk evaluation method, which combines picture fuzzy sets (PFSs), the PF-linear programming model (PF-LPM) method and the PF-weighted aggregated sum product assessment (PF-WASPAS) method. We adopt PFSs to evaluate risks of products. In order to overcome drawback of the traditional distance between PFSs, some new distance measures between PFSs based on the Dice similarity and the Jaccard similarity are proposed by us. The PF-LPM method which considers the subjective weights of risk factors and calculates synthetical deviation with the Dice similaritybased distance is utilized to calculate the weights of risk factors. Moreover, the PFWA operator and the PFWG operator are used by us to fuse experts' evaluation information. Then, the PF-WASPAS method is utilized to rank failure modes. Finally, an illustrative example with respect to pallet exchange rack is introduced, and the rationality, effectiveness and applicability of the proposed method are verified by a discussion and comparison.

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