![]() health/medicine, automotive, food safety, environmental monitoring, and agriculture). In recent years, there has been an increase in the number of electrochemical and optical sensors in various industrial applications (i.e. Since DL gained momentum after 2012, a significant portion of the chosen papers are from the last five years. From the results obtained, relevant papers were selected, giving priority to peer-reviewed journals and papers with more than 10 citations. An example search phrase constructed this way is, "Anomaly Detection using LSTM for Equipment Health Monitoring". Several combinations of the keywords from each column of Table I were used to search in Google Scholar and Web of Science. For this research, we first created a table of keywords as mentioned in Table I. Also, there are several types of sensors, various types of analysis required for prognosis and a lot of DL algorithms to choose from. The language used to refer to Industrial IoT is not consistent throughout the world. It is not an exhaustive survey of all DL methods but the ones that are relevant to this domain and the most effective. The goal of this paper is to review DL methods that are applicable to sensor data in the context of PM. A schematic of such a system is shown in Fig. The analyzed data is used by the applications in the organization. In summary, the PM process involves data acquisition from various kinds of sensors, data transmission and storage, data pre-processing and then analysis. In this paper, we briefly explain the DL approaches for PM. The emergence of DL algorithms has resulted in much better accuracy in prognosis. 10 has discussed the importance of ML algorithms for electrochemical engineers. Deep Learning (DL) refers to a class of ML algorithms that use neural networks with several layers of processing units. The algorithms learn a representation of the training data, which is then used to make predictions on out-of-sample data. ML offers algorithms that learn from data. In recent times, data-driven prognosis methods have proven to be very effective due to the availability of better data acquisition methods, Internet of Things (IoT) and Machine Learning (ML). While diagnosis involves identifying the cause of an existing problem, prognosis is predicting the occurrence of a problem and its cause. The most important goal of PM is to recognize uncommon system behavior and to have an early warning for catastrophic system damage. The benefits of PM include reduced downtime, improved quality, reduction in revenue losses due to equipment damage, better compliance, reduced warranty costs and improved safety of operators. A recent report from "Allied Market Research" predicted that the market for PM will be worth $23 billion by 2026. 1 PM is one of the most important components of smart manufacturing and Industry 4.0. Continuous monitoring of such variables, predicting failures or degradation and taking actions to prevent them is referred to as Predictive Maintenance (PM). For example, the oil temperature going beyond the normal range can cause the engine to stop functioning. Analysing sensor data that captures these variables can reveal several things such as the health of the equipment and potential failures.Įngine and equipment failures are often associated with the internal or environmental variables exhibiting unexpected behavior. At the same time certain environmental variables such as external temperature and humidity also change. When an engine or heavy industrial equipment is operating, certain internal physical quantities such as oil temperature, oil pressure etc., change significantly. Further, if the data can be transmitted to the processing unit with minimal delay, a real-time analysis can be performed to gain valuable insights. If such measurements are made repeatedly and stored, the behavior of the physical quantity can be studied. This makes it possible to measure physical quantities in the environment. Sensors convert physical signals into electrical signals. ![]()
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