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When a process experiences an out-of-control condition, identification of the change point is capable of leading practitioners to an effective root cause analysis. The change point addresses the time when a special cause(s) manifests itself into the process. In the statistical process monitoring when the chart signals an out-of-control condition, the change point analysis is an important step for the root cause analysis of the process. This paper attempts to propose a model approaching the artificial neural network to identify the change point of a multistage process with cascade property in the case that the process is modeled properly by a simple linear profile.

In practice, many processes can be modeled by a functional relationship rather than a single random variable or a random vector. This approach of modeling is referred to as the profile in the statistical process control literature. In this paper, two models based on multilayer perceptron (MLP) and convolutional neural network (CNN) approaches are proposed for identifying the change point of the profile of a multistage process.

The capability of the proposed models are evaluated and compared using several numerical scenarios. The numerical analysis of the proposed neural networks indicates that the two proposed models are capable of identifying the change point in different scenarios effectively. The comparative sensitivity analysis shows that the capability of the proposed convolutional network is superior compared to MLP network.

To the best of the authors' knowledge, this is the first time that: (1) A model is proposed to identify the change point of the profile of a multistage process. (2) A convolutional neural network is modeled for identifying the change point of an out-of-control condition.

Different real cases indicate that the quality of a process is better monitored by a functional relationship rather than the traditional statistical process control (SPC) methods. This approach is referred to as profile monitoring. A serious objective in profile monitoring is the sensitivity of a model to very small changes of the process. The rapid progress of the precision manufacturing also indicates the importance of identifying very small shift types of a process/product profile curve. This sensitivity allows one to identify the fault of a process sooner compared to the case of lack of the capability.

This paper proposed a new method to monitor very small shift types of a polynomial profile for phase II of the SPC. The proposed method was named as MGWMA-PF. The performance capability of the proposed approach was evaluated through several numerical examples. A real case study was also used to investigate the capability of the proposed model.

The results addressed that the proposed method was capable of detecting very small shift types effectively. The numerical report based on the average run length (ARL) term revealed the more sensitivity of the proposed model compared to other existing methods of the literature.

This paper proposes a new method to monitor very small shift types of a polynomial profile for phase II of the SPC. The proposed method provides detecting a very small change manifested itself to the process.

This paper addresses special reliability-centered maintenance (RCM) strategies for one-shot devices by providing fuzzy inferences system with the assumption that, to data, there is no data available on their maintenance. As far as one-shot devices are concerned, the relevant data is inadequate.

In this paper, a fuzzy expert system is proposed to effectively select RCM strategies for one-shot devices. In this research: (1) a human expert team is provided, (2) spatial RCM strategies for one-shot devices and parameters bearing upon those strategies are determined, (3) the verbal variables of the expert team are transformed into fuzzy sets, (4) the relationship between parameters and strategies are designed whereupon a model is developed by MATLAB software, (5) Finally, the model is applied to a real-life one-shot system.

The finding of this study indicates that the proposed fuzzy expert system can determine the parameters affecting the choice of the appropriate one-shot RCM strategies, and a fuzzy inference system can help for effective decision making.

The developed model can be used as a fast and reliable method for determining an appropriate one-shot RCM strategy, whose results can be relied upon with a suitable approximation in respect of the behavior test. To the best authors’ knowledge, this problem is not addressed yet.