# Prediction of Machine Failure in Industry 4.0: A Hybrid CNN-LSTM Framework
## 2 Related Work  
Data-driven approaches have attracted many research efforts in the area of smart manufacturing. Machine learning techniques for manufacturing applications, along with their weaknesses and strengths, are explained in [19; 20]. Successful machine learning algorithms such as Bayesian Networks, artificial neural networks, and other ensemble methods are explained in [21]. Traditional machine learning algorithms such as logistic regression, artificial neural networks, or support vector machines yield a modest performance because of their shallow structural design and hand-crafted feature engineering [22]. Deep learning has exhibited impressive performance in fields such as image classification, natural language processing, semantic segmentation, and object detection/recognition [23]. Deep learning is best known for automatically extracting highly nonlinear and complex abstract features by means of multiple layers stacked on top of each other [24]. Because of its automated feature extraction and ability to learn different levels of data abstractions, deep learning can identify hidden patterns and trends in data and predict them through a well-defined optimization pipeline. The authors of [25] introduced long short-term memory (LSTM), a special kind of RNN, capable of dealing with complex data and learning long-term dependencies. Over time, these networks were further optimized in other work [26; 27]. LSTMs have demonstrated their success in time-series forecasting [28]. Recently in [29], a CNN combined with K-means, which was proposed for time-series load forecasting, achieved impressive results. The main reason for this is the capability of a CNN to extract features from input data at different levels. Because a CNN has the ability to learn for a vast range of non-linear problems as explained in [22], it is well suited for time-series production data. In contrast, LSTMs are effective in modelling time-series data because of their ability to remember the states of input data in their memory [28]. Deep learning method with LSTM combined with support vector machine (SVM) proposed in [30] are utilized to distinguish aberrant data from normal vibration signals gathered during a reduction gearbox endurance test and helicopter test flight data acquired by many sensors located throughout the aircraft. A method based on JDA and deep belief network (DBN) for fault bearing diagnosis was proposed in [31]. The JDA is used in the JACADN to perform feature transfer between source domain samples and target domain samples, i.e., the kernel function maps the source domain samples and target domain samples into the same feature space. Anomaly detection using a generative adversarial network (GANS) for industrial sensor data was proposed in [32]. The paper focused on reconstruction of sound signal reconstructions and detection of anomalies. The basic notion underlying the techniques is that normal conditions can be accurately reconstructed using a smaller latent space interim of neural network design, whereas abnormal conditions cannot be reproduced due to larger reconstruction losses. This method is appropriate for anomaly detection, as the volume of anomalous condition data is typically much smaller than normal condition data, and a detection model may be trained solely using normal condition data.  
The use of deep learning techniques to detect anomalies has improved the results of older methods. An artificial neural network model underpins deep learning. Deep learning and LSTMs offer to train hierarchical models over input data that describe probability distributions. Artificial intelligence has become a vibrant subject with many practical applications such as machine prognosis, fault detection, predictive maintenance, etc., thanks to recent advancements in both hardware and brain models, particularly in the previous decade.