如何妥善計畫設備維修一直都是生產線上一個重要的課題，從預防性維修到預測性維修，其目的都在於降低生產線之非預期停產的機率，從而達到降低成本以及工安意外的發生。本研究主要著重在探討預測性維修的方式，在特徵擷取的部分使用了12個時域特徵值、5個頻域特徵值以及8個時頻域特徵值，在特徵選擇的部分使用基因演算法從所有的特徵值中挑選出最適當的特徵值作為模型的輸入資料，而在模型部分使用獨立遞歸神經網路評估設備的健康狀況。本研究提出結合超啟發式演算法及深度學習的技術，將之應用在預測滾珠軸承之剩餘使用壽命，並期望能呈現好的結果。
為了驗證本研究所提出的整合預測模型是否有效，本研究使用IEEE 2012 PHM Data Challenge competition競賽中所提供之公開資料集，並且與當時的冠軍Sutrisno et al. (2012)、Guo et al. (2017)、遞歸神經網路以及獨立遞歸神經網路共四種方法之結果進行比較。實驗證明，想對於其他四個方法，以啟發式演算法為基礎的獨立遞歸神經網路可獲得較佳的預測結果。

Planning maintenance of facilities is the main topic for the production line. From preventive maintenance to predictive maintenance, the main purpose is cost down by reducing the chance of the unexpected shot down. Thus, this study intends to propose genetic algorithm-based independent recurrent neural network (GA-based IndRNN), which is a kind for deep learning techniques, and apply it to predict remaining useful life for the ball bearings using vibration signals. Totally, 25 features extracted from three domains are considered. They include 12 features in time domain, 5 features in frequency domain and 8 features in time-frequency domain. In addition, for feature selection, genetic algorithm (GA) is utilized to choose the most adaptive feature subset for the model.
To validate the proposed method, this study uses the dataset which is provided by IEEE 2012 PHM Data Challenge competition. The result of the proposed method is compared with four methods by the winner of the competition, Sutrisno et al. (2012), Guo et al. (2017), RNN and IndRNN. The experimental results indicate that GA-based IndRNN is able to perform better than the other methods.

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