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This paper proposes a time‐varying failure mode and effect analysis (FMEA) method based on interval‐valued spherical fuzzy theory, which not only improves the limitations in evaluating, weighting, and ranking but also considers the effect of time change. The process of distinguishing time changes enables the FMEA to have dynamic recognition capability, enabling it to identify critical failure modes more accurately. The interval‐valued spherical fuzzy theory is used to deal with the uncertainty of intuitionistic linguistic evaluations. The advantages of two traditional approaches are combined to improve the weight determined method. Risk factors are divided into subjective and objective types. In the subjective risk factors, which are severity (S) and detection (D), the consistency of judgment is used as the acceptance standard. In the objective risk factors, which are occurrence (O), the time‐varying characteristics are considered. The occurrence in a certain period is expressed as the integral of failure intensity in the time period. Interval‐valued spherical fuzzy exponential risk priority number is proposed as the criterion for measuring the priority of failure modes. The effectiveness of the proposed method is verified using an example of spindle.

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The NC machine tool are subjected to a variety of working load during the actual machining process. The influence of different kinds of working loads on the NC machine tool’s reliability level are different. Therefore, this paper proposes an importance evaluation method of NC machine tool working loads based on analytic hierarchy process (AHP) -fuzzy comprehensive evaluation to judge the influence of different kinds of working loads on the reliability level. Firstly, the hierarchical structure of the importance evaluation of NC machine tool working loads is established, and then the structure of discrimination matrix and the consistency test of the matrix are determined. Secondly, the fuzzy membership R of the fuzzy subset is established, and then the weight vector M of the evaluation factor and the evaluation result vector S of the fuzzy comprehensive evaluation are determined to realize the quantitative analysis of the fuzzy object. Finally, the importance of calculating the machining center load is ranked as cutting force, feed rate, torque, tool changing frequency, temperature and noise in the example.