# Optimization of Design Parameters in LSTM Model for Predictive Maintenance
## 1 Introduction  
Maintenance is essential for keeping an ideal working state of machinery, and its importance is emphasized in state-of-the-art manufacturing processes whose risk cost is considerable. Lack of maintenance may result in the frequent breakdown of machinery, whilemoderate maintenance causes a huge burden of expenses and a decline in productivity, indicating the importance of suitable maintenance execution. There are some perspectives of maintenance approaches named reactive, planned, and predictive. A reactive approach means that an action is carried out after failure and a planned one involves carrying out maintenance according to a pre-defined schedule. Predictive maintenance involves performing maintenance tasks based on prognosis, which means predicting the likely or expected progress of events. In other words, repairing or replacement can be properly fulfilled by considering the remaining useful life estimated by a systematic model. Figure 1 depicts the financial costs and breakdown rates of machinery by the abovementioned approaches [1]. Among these three approaches, predictive maintenance is the most dominant one in terms of both expense and productivity. Figure 1: Costs and loss of productivity according to maintenance approaches. Predictive maintenance can be classified into physics model-based, statistical model-based, and artificial intelligence (AI) model-based approaches. According to [PERSON] et al. (2018), nearly two-thirds of the existing publications are about physics and statistical model-based approaches, which have their own limitations. Firstly, users who want to apply a physics model-based approach should understand the internal mechanism of failure since this approach makes predictions by means of a certain equation or principle of physics. Moreover, such comprehension can hardly be achieved for the prediction of machinery containing complex components affected by different principles. Secondly, the statistical model-based approach carries out prediction by devising a statistical model rather than depending on a particular theory of physics. However, the assumption of a model should be satisfied for applying this method, and there is no baseline for parameters belonging to a model, which impedes the extensive application of the statistical model-based approach. Finally, the AI model-based approach is the most recent approach, employing AI methodologies including models used for machine learning or deep learning. This approach can guarantee a more superior performance than the aforementioned approaches, owing to its formal semantics and iterative learning procedure. In particular, implementation of deep learning enables us to obtain a very elaborate model for predictive maintenance by capturing implicit but primary patterns of machinery data. However, one thing to consider before using this approach is the method of hyperparameter tuning because this can bring about a deviation of the performance. For example, although an exhaustive grid search or random search can be considered as an elementary approach, they are ineffective and it is hard to guarantee optimal hyperparameters. Therefore, other efficient methods such as metaheuristics should be used to explore the solution space as more refined alternatives for hyperparameter optimization. Meanwhile, for successful predictive maintenance in the AI model-based approach, a model describing the state of machinery should be properly designed due to its nature of estimating future failure events. Specifically, the predictive maintenance model can be developed by applying three steps, as follows ([PERSON], 2018): (i) derivation of a health indicator (HI), (ii) definition of the health stage (HS), (iii) generation of the prediction and monitoring model. Firstly, the HI is drawn from collected raw data and used for modeling the state of machinery. Then, the HS is defined to discriminate the state of machinery as a healthy stage or unhealthy stage caused by any kind of fault.