# Prediction of Machine Failure in Industry 4.0: A Hybrid CNN-LSTM Framework
## 1 Introduction
Many papers have been published that cover machine fault diagnosis to a large extent over the past few years. A detailed overview of a deep learning-based monitoring system for machine health was proposed in [11]. A simple auto-encoder, CNN and RNN-based machine health monitoring system was proposed in [12]. A prognostic and health management architecture based on deep learning was proposed in [13]. An auto encoder-based CNN for induction motor diagnosis was also proposed by [14]. A gated CNN to estimate the remaining life of a machine was proposed in [15]. [PERSON] et al. [16] used LSTM to predict the remaining life of turbofan engines. Deep learning has been studied to predict an unknown time series using historical data [17]. A variety of modern machine learning algorithms ranging from multi-layer perceptrons (MLP) and long short-term memory (LSTM) to the combinations of RNNs and dynamic Boltzmann machines [17] have been used in time-series forecasting. All of these algorithms have their own strengths and weaknesses, depending on the type of data or the task at hand. However, due to the complex nature of predictive maintenance use cases, none of the above noted algorithms have provided excellent results. Moreover, hybrid methods [18] are a new development trend in the field of time-series forecasting. In this paper, we propose a hybrid deep learning framework based on CNN-LSTM for PdM. The proposed method combines the advantages of convolutional neural networks (CNN), which extract the effective features and patterns among multi-variate input variables, and long short term memory (LSTM), which captures the complex long-term dependencies and automatically selects the mode best suited for relevant time-series data. The hybrid CNN-LSTM model is designed to make the optimization easier by extracting the effective features using CNNs and then incorporating LSTM in parallel to predict machine failures. The main contributions of our work are summarised as follows:  
* We propose a hybrid deep learning model (CNN-LSTM) for PdM. The model uses CNN to extract features from the time series and LSTM for prediction. It uses a time sequence of different errors and analyses the correlation between different input variables for better prediction. * We introduce a novel temporal skip connection component for LSTM that enables them to capture long length dependencies and makes optimization easier and efficient. * On comparing the evaluation indexes of CNN, LSTM, and hybrid CNN-LSTM, we found that our hybrid CNN-LSTM has the highest prediction accuracy and is more reliable and suitable for PdM forecasting. The IoT sensors and CCTVs are responsible for collecting data from the different available equipment on a production floor. Efficient use of data with AI encourages innovation, better productivity, and technology dissemination. Structure of the Paper: The rest of the paper is organized as follows. Section 2 provides related work of the state-of-the-art data-driven approaches. Section 3 throws some light on the Industry 4.0 use case. Sections 4 and 5 provides some background to existing methodologies, including CNN and LSTM, used in the design of the model and presents our framework by explaining the structural design of the model, respectively. Section 6 explains the results and the optimization strategy adopted in this study. Section 7 explains the lesson learned throughout the course of this study. Section 8 discuses the limitations of the proposed framework, and, finally, Section 9 concludes the paper.