# Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events
## 2 Related Work
### Problem of Industrial Maintenance  
As the Industrial Internet of Things (IIoT) technology develops rapidly, companies have the ability to observe the health of engine components and manufactured systems through the collection of signals from sensors. According to the results of IIoT sensors, companies can build systems to predict component conditions. Practically, the components are required to be serviced or replaced before the end of their life while performing the work assigned to them. Predicting the life state of a part is so important for industries that intend to grow in a fast-paced technological environment. Recent studies on predictive maintenance help industries to create an alert before parts fail. By detecting component failures, companies have the opportunity to keep their operations efficient while reducing their maintenance costs by repairing components in advance. Since maintenance directly affects productivity and service quality, optimized maintenance is the key factor for organizations to allow them to generate more revenue and remain competitive in the growing industrialized world. With the help of a well-designed predictive system to understand the current state of an engine, components could be taken out of service before a malfunction occurs. With the help of inspection, effective maintenance extends component life, improves equipment availability, and keeps components in good condition while reducing costs. Real-time data collected from sensors are an excellent resource for components' live-cycle modeling. Markov chain models [31], survival analysis for machinery lifespans [32], ML optimization algorithms [33], and various machine learning approaches have been applied to model predictive maintenance [34]. For solving the prediction task, machine learning (ML) technology is increasingly being used. However, the state of recent research is not well posed and there is a lack of adoption of up-to-date models for pre-processing and training [16]. The incorporation of the uniform use of ML algorithms for Industry 4.0 classification detection and prediction processes is also at risk. The authors of [35] propose the use of long short term memory (LSTM) networks to predict the current state of a motor. The LSTM model deals with sequential input data. The training process of LSTM networks is performed on a large-scale data processing engine with high performance. Since the huge amount of data flow into the prediction model, Apache Spark, which offers a distributed clustering environment, has been used. The output of the LSTM network decides the current life state of the components and offers alerts for components before their end of life. To predict the current state of any system unit, condition-based maintenance (CBM) has been proposed. According to [PERSON] et al. [36], CBM recommends actions based on the information collected by the system. The main objectives of CBM are to avoid unnecessary maintenance actions and to recommend maintenance actions if an anomaly is detected. Estimating the remaining useful lifetime (RUL) with high accuracy is crucial to developing an effective CBM strategy. RUL could be predicted by collecting signals with sensors located in relevant units of the system. Furthermore, the use of deep learning RNNs and, more specifically, LSTM is considered significant for the prediction of machinery's remaining useful life [37]. [PERSON] et al. [38] developed artificial neural networks (ANN) to predict RUL under unknown initial wear. [PERSON] et al. [38] proposed an ANN-based approach to more accurately predict the RUL of high-speed milling cutters. The proposed model was built based on temporal statistical characteristics. [PERSON] et.al proposed a new approach for RUL estimation called meta cognitive regression neural network (McRNN) for function approximation. McRNN uses extended Kalman filters (EKF) to find the optimal training of network parameters [39].