A key aspect of life cycle management for pursuing sustainability is machine condition prognosis, which requires a condition monitoring system that estimates machine system deterioration to assist engineers in determining which maintenance actions to take. Conventional data-driven methods such as machine learning, have two issues. One is data dependency. The accuracy of a data-driven method depends on the data volume because a data-driven method builds a classification model on the basis of historical data as training data. However, it is difficult to acquire enough data on all deterioration modes, which requires a long time, because deterioration modes are diverse, and some of them rarely happen. The other issue is interpretability. When a condition monitoring system using a data-driven method sends the degree of deterioration (DoD) of the machine system to maintenance engineers, they have difficulty in understanding the results because the method is a black box. The objective of this paper is to address these two issues. We propose a model-based method that simulates machine system deterioration with a cyber physical system (CPS). Model-based methods address these issues in the following manner. First, the methods can simulate the progress of deterioration from an initial condition to failure to estimate the DoD. Second, the methods employ mathematical models that represent machine systems. Engineers create such mathematical models (which we call “physical models”) by referring to various kinds of knowledge like design information and the result of failure mode and effects analysis. A physical model allows us to reason about a machine system to address interpretability. For dealing with machinery that has multiple operation modes, we introduce a state space to clarify the relationship among input, observable state variables, and DoD in a physical model. The CPS estimates DoD by comparing observed data with simulated data in the state space. In our case study, we evaluated our proposed method with a hydraulic pump of a mining machine. First we created a physical model with Modelica, which is a multi-domain modeling language. Then, the method constructed the state space by simulating deterioration with the physical model given all combinations of inputs and DoD. After that, we showed that the estimated DoD tended to increase until the hydraulic pump was replaced, using the observed data from an actual mining machine. As a result, the experimental results revealed that the proposed method succeeded in identifying the DoD with observed data of the hydraulic pump of a mining machine.
Knowledge discovery for friction stir welding via data driven approaches
