Abstract
Household appliances, climate control machines, vehicles, elevators, cash counting machines, etc., are complex machines with key contributions to the smart city. Those devices have limited memory and processing power, but they are not just actuators; they embed tens of sensors and actuators managed by several microcontrollers and microprocessors communicated by control buses. On the other hand, predictive maintenance and the capability of identifying failures to avoid greater damage of machines is becoming a topic of great relevance in Industry 4.0, and the large amount of data to be processed is a concern. This article proposes a layered methodology to enable complex machines with automatic fault detection or predictive maintenance. It presents a layered structure to perform the collection, filtering and extraction of indicators, along with their processing. The aim is to reduce the amount of data to work with, and to optimize them by generating indicators that concentrate the information provided by data. To test its applicability, a prototype of a cash counting machine has been used. With this prototype, different failure cases have been simulated by introducing defective elements. After the extraction of the indicators, using the Kullback–Liebler divergence, it has been possible to visualize the differences between the data associated with normal and failure operation. Subsequently, using a neural network, good results have been obtained, being able to correctly classify the failure in 90% of the cases. The result of this application demonstrates the proper functioning of the proposed approach in complex machines.
Highlights
Accepted: 11 January 2022Predictive maintenance is a recent technique, the result of the evolution of maintenance techniques over the years
Is reached, being able to identify the failures with a smaller amount of information and being able to implement this methodology in complex machines with limited capacities
Work, aa study study of of the the state state of of the the art art of of predictive maintenance techniques, techniques, In predictive maintenance focused on industrial environments, has been carried out
Summary
Predictive maintenance is a recent technique, the result of the evolution of maintenance techniques over the years. There are many works proposing preand centrally analyzes them In complex machines, this is not possible due to memory and dictive maintenance strategies in production lines [6] or industrial equipment [7,8]. Approaches gather all the data together in edge/fog devices [9] or in the cloud [10] and Currently, there are different predictive maintenance strategies on whether centrally analyzes them In complex machines, this is not possibledepending due to memory and they focus onrestrictions physical aspects (physical on aspects of knowledge of the computation of controllers andmodel-based), to industrial bus bandwidth limitations.
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