International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

Approaches to machine and equipment maintenance based on data analytics and artificial intelligence are trending in modern manufacturing. These methods are used to predict the remaining useful life (RUL) of equipment and thus enable forward maintenance planning. However, for predictive maintenance systems, it is also necessary to detect anomalies in operation and classify the occurring errors. Classical approaches of supervised machine learning are often in this case unusable because those methods require a large amount of run-to-failure data (R2F), which is often not possible to collect due to the undesirable character of failure states in the manufacturing process. The paper presents and tests several methods of detecting device fault states using an autoencoder network, which offers a beneficial solution in the case of the unavailability of R2F data in the system.

The article focuses on the definition of industrial business risks that are associated with risk management. Following the description of the risks, the article then focuses on the elimination of the hazard or the reduction of each of the two criteria that determine the risk in question, such as the severity of the damage caused by the hazard and the likelihood that the damage will occur, separately or simultaneously, are two ways to achieve the purpose of risk reduction. The article is highly relevant to the implementation and development of technical systems and equipment.