Heavy-duty machine tools are composed of many subsystems with different functions, and their reliability is governed by the reliabilities of these subsystems. It is important to rank the weaknesses of subsystems and identify the weakest subsystem to optimize products and improve their reliabilities. However, traditional ranking methods based on failure mode effect and critical analysis (FMECA) does not consider the complex maintenance of products. Herein, a weakness ranking method for the subsystems of heavy-duty machine tools is proposed based on generalized FMECA information. In this method, eight reliability indexes, including maintainability and maintenance cost, are considered in the generalized FMECA information. Subsequently, the cognition best worst method is used to calculate the weight of each screened index, and the weaknesses of the subsystems are ranked using a technique for order preference by similarity to an ideal solution. Finally, based on the failure data collected from certain domestic heavy-duty horizontal lathes, the weakness ranking result of the subsystems is obtained to verify the effectiveness of the proposed method. An improved weakness ranking method that can comprehensively analyze and identify weak subsystems is proposed herein for designing and improving the reliability of complex electromechanical products.
Vibration signals of rolling element bearings (REBs) contain substantial bearing motion state information. However, the property of nonlinear and nonstationary vibration signals decreases the diagnostic accuracy of REBs. To improve the accuracy of fault diagnosis for REBs, an ensemble approach based on ensemble empirical mode decomposition (EEMD), multi-scale permutation entropy (MPE), and backpropagation (BP) neural network optimized by genetic algorithm (GA) is proposed. Firstly, the REBs are decomposed into a set of intrinsic mode functions (IMFs) that contain various fault features by EEMD. The fault features of the first four IMFs are extracted by MPE, and the feature vectors are formed. Then, the BP neural network optimized by GA is utilized as a classifier for fault diagnosis to train and test the feature vector set, and the fault diagnosis of the REBs is realized in the form of probability output. Experimental results show that the proposed method can identify the fault pattern of the vibration signals of REBs precisely. Compared with the existing fault diagnosis methods, the proposed method can realize the fault diagnosis of REBs with 16 fault patterns, and demonstrates an excellent accuracy rate.
Traditional reliability assessment of spindle systems of machine tools suffers from long testing time and high cost. Accelerated life testing is an alternative that overcomes the shortcomings of traditional reliability testing. In a life testing, identification of critical factors of service life and an accurate model are important. Based on the characteristic analysis and engineering experience, four reliability factors, which are the average power of spindle systems, the number of tool changing, the number of spindles restarting and environment temperature, are selected as accelerating environment variables. An accelerated failure time model is used to describe the inverse relationship between the variables and reliability for the catastrophic failure mode and the degradation failure mode separately. Then a competing risk model is built by considering competing risks of two modes. Parametric reliability models are proposed to capture the statistical independency and dependency separately, in which the Gumbel–Hougaard copula function is used to establish the joint cumulative distribution for dependency. Thereby the hypothesis testing is developed to determine the failure modes dependency. The reliability sensitivity of each environment variable is analyzed. Finally, the proposed model is illustrated with a real field case study.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.