# Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events
## 1 Introduction  
The rapid evolution of Industry 4.0 [1], accompanied by the enormous amount of data collected from various sensors, devices, machines, or embedded systems, is increasing the research and industrial communities' needs for intelligent systems, and eventually will lead us to the arrival of the Industry 5.0 era. Until now, the ancestor of Industry 5.0, the digital Industry 4.0, has benefited from the use of the Industrial Internet of Things (IIoT), Big Data, cloud computing, and Augmented Reality, which will be followed by the exploitation of the encapsulated knowledge via Artificial Intelligence [2] and more precisely through machine learning and deep learning techniques. Gathering data from a set of IIoT sensors necessitates a suitable control unit. Hence, two main systems appear in the industry, decentralized control systems (DCS) and programmable logic controllers (PLC). Furthermore, the storage and analysis of the collected Big Data [3] require distributed database management systems (DBMS) as a unified data point of origin, implementing artificially intelligent logic and cloud services. Taking into account the gathered industrial sensory data, it is unquestionable that much knowledge is encapsulated in them. The extraction of patterns, correlations, and outliers included in these collections are tasks which humans can hardly process. Consequently, automated, ingenious, and highly productive practices are in great demand to exceed human limitations while decreasing engine failure and increasing productivity. The appearance of machinery component malfunctions and critical events are two mandatory scenarios frequently revealed in an industrial environment. Therefore, the operation status of several delicate machinery parts, such as pumps, compressors, and robotic arms, must be kept under surveillance, predominantly when they work under high temperatures, pressures, or/and strict performance indices defined by manufacturing requirements [4]. Focusing on decision making, machine learning (ML) techniques and, more precisely, data mining [5] and regression [6], are broadly used [7; 8], leading to robustness in industrial maintenance, detecting the majority of possible faults through pattern recognition and triggering a proper alert. Deep and machine learning algorithm operationalization is different from traditional algorithm deployment. Therefore, thoroughly evaluating machine learning algorithms before production is an important validation of their correct operation. Such validation includes formal reasoning over all possible inputs or property checking that all industrial responses/ behavioral requirements are captured via formal methods [9; 10], and their practical implementations over appropriate representational languages or tools [9; 11]. The verification of the strict implementation of operations and their response using validation tools should also be addressed. Model checking, model-based testing using formal operational test scenarios, and design by refinement and abstract interpretation during training and validation will lead to robust deep learning models [11]. There are three state-of-the-art categories of algorithms for industrial maintenance and machinery operations:  
**Classical ML or deterministic methods:** This category includes algorithms such as linear regression, fuzzy control, threshold control, proportional integral derivative (PID) control, support vector machines (SVM), decision trees, random forest, etc. These algorithms are currently in use by most modern industries and machinery maintenance software for classification and regression purposes. Nevertheless, their appliance is of a specific use and targets maintenance cases, with different hyperparameter values for each case that requires accurate calibration;  
**Narrow depth ML methods:** This category includes ML networks of limited depth and techniques of targeted patterns detection. Gradient boosting networks such as LightGBM, and neural networks of limited and fixed depth are corresponding methods of this category.