# Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events
## 2 Related Work
### Problem of Critical Events Detection  
With rapid advances in information and communication technology, log files, which are time records of the occurrence of various types of events, are commonly available at both machine and system levels in industrial enterprises. For example, the service department of most manufacturers keeps a record of after-sales service provided for products during the warranty period. These records contain the occurrence of malfunction/failure events and the corresponding repair actions taken on the products over time. Many modern machines are numerically controlled by on-board computers, and various events such as machine activities, critical failures, operator/user actions, and job status are recorded in real-time during machine operation. For example, for hardware handling equipment, we can receive an event log containing various types of events, such as battery status, accident occurrences, internal communication errors between subsystems, etc. [12]. By modeling the relationship between the precursor events and the critical event, we could predict the occurrence of the critical event when we observe the precursor events. Another example is that, by analyzing the service records of a particular product, we can find the frequency of occurrence of certain failure modes and the possible relationship between different failure modes. For example, two different failure types may be related because of an underlying physical connection between the two failure types or because these two failure modes are caused by a common root cause. Information about the relationship between events, when combined with the ability to accurately predict critical events, is very useful in identifying the root causes of failures and in designing optimal preventive maintenance policies that can reduce unexpected machine downtime and maintenance costs. Thus, the establishment of a method of modeling and predicting events based on the event log can be particularly valuable in industrial practice. Deterministic solutions for industrial maintenance and critical events detection focus on using thresholds or PID (proportional integral derivative) controllers coefficients or fuzzy rules and parameters. Unfortunately, such approximations are insufficient due to their static nature, resulting in their inability to capture patterns even among the same machines. Furthermore, fuzzy logic techniques [13; 14], e.g., using Gaussian models, are still deterministic, sometimes lacking rules and physical interpretation. However, it can perform adequately in sensory responses with no annotated output feedback [15]. On the other hand, other methods, such as linear or non-linear regression models [6], are limited in performance due to their absence or the static use of data processing depth and the appliance of uniform pre-processing methods towards the input sensory data or equipment [16]. Significant progress has been made recently in machine learning and artificial intelligence [17; 18]. Many new general data-driven modeling approaches have been developed, among which deep learning methods have proven themselves quite flexible and strong. Deep learning is a general method of approximating nonlinear functions that uses a neural network framework, which can learn, from data, the relationship between high-dimensional inputs and output. The effectiveness of deep learning comes from its flexible structure. Recent advances in stochastic gradient descent (SGD) optimization and GPU-based parallel computing enable very large-scale deep learning models, thus enhancing the flexibility and efficiency of deep learning models. Narrow depth networks, specifically MLPs, are also used in industrial maintenance focusing on real-time event detection. The authors of [19] present two MLPs of one and four hidden layers accordingly for predicting gas turbine and compressor decay states. [PERSON] et al. present an MLP model for predicting potential machinery faults [20]. [PERSON] et al.