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The self-organizing fuzzy (SOF) logic classifier is an efficient and non-parametric classifier. Its classification process is divided into an offline training stage, an online training stage, and a testing stage. Representative samples of different categories are obtained through the first two stages, and these representative samples are called prototypes. However, in the testing stage, the classification of testing samples is completely dependent on the prototype with the maximum similarity, without considering the influence of other prototypes on the classification decision of testing samples. Aiming at the testing stage, this paper proposed a new SOF classifier based on the harmonic mean difference (HMDSOF). In the testing stage of HMDSOF, firstly, each prototype was sorted in descending order according to the similarity between each prototype in the same category and the testing sample. Secondly, multiple local mean vectors of the prototypes after sorting were calculated. Finally, the testing sample was classified into the category with the smallest harmonic mean difference. Based on the above new method, in this paper, the multiscale permutation entropy (MPE) was used to extract fault features, linear discriminant analysis (LDA) was used to reduce the dimension of fault features, and the proposed HMDSOF was further used to classify the features. At the end of this paper, the proposed fault diagnosis method was applied to the diagnosis examples of two groups of different rolling bearings. The results verify the superiority and generalization of the proposed fault diagnosis method.

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Little work has been done to assess the reliability of a vital system like the manufacturing system. In this article, a novel and effective system reliability evaluation method in terms of failure losses has been proposed for manufacturing systems of job shop type, and then the failure losses based component importance measure (CIM) is used for importance analysis of equipment. The former indicates the present system reliability situation and the latter points the way to reliability improvement efforts. In this scheme, the problem is described and modeled by a dynamic directed network. Consider that the actual processing time of machines is to contribute to failure occurrence, it is used to calculate the failure times and failure losses. The obtained total failure times and failure losses of the system are applied to evaluate its reliability. Techniques to estimate two kinds of failure losses based CIMs are presented. They offer guidelines to realize system reliability growth cost-effectively. A case study of a real job shop is provided as an example to demonstrate the validity of the proposed methods. Comparison to other commonly used methods shows the efficiency of the proposed methods. CNCComputer numerical control DNC Distributed numerical control ERP Enterprise resource planning FT Failure times MES Manufacturing execution system MPT Machine processing time MTBF Mean time between failure MTTF Mean time to failure MTTR Mean time to repair OEE Overall equipment effectiveness RFID Radio frequency identification WDN Weighted and directed network WIP Work-in-process t, T Sequence number of time intervals (number of all intervals is T ) i, I t Sequence number of products items (number of all items is I t ) j, J t Sequence number of processes during t (J t is the maximum value) k, K Sequence number of equipment (number of all equipment is K ) G t WDN during t M k Equipment k r k Failure rate of M k F T tk Failure times of M k during t T total Total time V ti Volume size of item i during t B ti j Processing time of item i for the jth process during t x ti jk Be equal to 1 if M k is used for machining the jth process of item i during t, 0 otherwise MT B F k MTBF of M k MT T R k MTTR of M k C k Average maintenance cost for every repair process of M k 123 J Intell Manuf P L tk Production losses for M k during time period t C f k

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