# Prediction of Machine Failure in Industry 4.0: A Hybrid CNN-LSTM Framework
## 1 Introduction  
In industry, maintenance is of crucial importance, as it directly impacts the cost, reliability, ability, quality, and performance of a company. Unwanted or unplanned downtime of equipment adds to the degradation and suspension of the core business, resulting in immeasurable losses and significant penalties.n It was reported that Amazon suffered a set back of $4 million in sales for a downtime of 49 min in 2013 [1]. According to a survey by the Ponemon Institute [2], on average an organization loses $138,000 per hour because of downtime. Also, it is reported that the operations and maintenance costs of offshore wind turbines amount to 20% to 35% of their total generated revenue [3], and for the oil and gas industry the maintenance expenditure can range from 15% to 70% of the total production cost [4]. It is therefore crucial for industries to look into efficient and competent methods to prevent unexpected downtime, hence reducing operation and maintenance costs and improving reliability. With the evolution of technology like the Internet of Things (IoT) [5], we are able to connect manufacturing devices to networks to send and exchange data. The first step for industry is to connect their machines and gather data from them. The data generated are collected and stored in the cloud and can later be utilized for analysis and visualization. Predictive maintenance has been discussed and researched for some time [6], but in recent years it has become more widely accessible because of the seamless advancements in modern technology [7]. It generally involves monitoring the operating conditions of the machines and gathering the information that could be used to maximize the interval time between repairs and to minimize unscheduled disruption due to machine failures [8]. Modern technology has the potential to identify and detect developing faults in machine components, predict a fault's progression, and finally to provide a strategy for maintenance schedules. Predictive maintenance is an important part of smart manufacturing and Industry 4.0. According to a recent report, the market for PdM will be worth $23 billion by 2026 [9]. The purpose of PdM is to identify uncommon machine behavior and to send out a warning about probable system damage. However, it still remains one of the key challenges faced by the industry. Additionally, with the advent of various technologies, many new concepts have been reinforced for PdM such as using IoT to gather increasing amounts of data from machines equipped with sensors [10], advanced techniques for data pre-processing, e.g., feature extraction, normalization, data cleaning, and preparation, etc., and machine learning and deep learning-based models for condition monitoring and failure detection. All of these improvements and discoveries have improved overall system reliability, reducing machine downtime and costs. Currently, most modern machines deployed in manufacturing production lines are equipped with advanced sensors and increased communications capabilities. These machines are capable of monitoring their status by continuously observing the conditions of various important features such as voltage, pressure, vibration, rotation, temperature, motor speed, etc. These features of a modern machine are used to train deep learning algorithms that are capable of detecting and predicting faults even before they occur. This allows us to augment or even replace an ordinary/regular maintenance schedule through predictive maintenance, which is more reliable and advanced, reduces unnecessary maintenance and its associated costs, and guarantees the proper and timely functioning of machines. Deep learning models have provided effective solutions in fault diagnosis due to their powerful feature learning abilities. They build a set of representation methods using numerous layers and learn the non-linear representation of any time series to a higher level of complexity and abstraction.