The digitalization of the economy in the past decades has made data availability grow and become more important. From the maintenance point of view, clients are more demanding, wanting systems that will not have breakdowns while reducing exploitation costs. This challenging scenario has pushed companies in the direction of more intelligent maintenance solutions that involve choosing the best course of action in terms of system availability. Nowadays, these solutions are usually called prescriptive maintenance. This term is vaguely defined and its use is often unjustified. In this article we will discuss what really characterizes prescriptive maintenance, review some of the work published with this term and propose a generic framework to guide the development of such solutions. In the end, we will illustrate the use of the generic framework in a practical case.
Mechanical faults occurring in drivetrains are traditionally
monitored through vibration analysis and, more rarely,
by analysing electrical quantities measured on the
electromechanical system involved. However, a monitoring
method that is able to take into account all of the information
contained in three-phase electrical quantities was recently
proposed. The goal of this paper is to compare this threephase
electrical approach with the usual vibration-based
method in terms of its capability to detect mechanical faults
in drivetrains. In this context, a 2 MW geared wind turbine
operating in an industrial wind farm was equipped with
accelerometers near the main bearing and electrical sensors
on the stator of the electrical generator for several months.
During this period, an important mechanical fault occurred
in the main bearing of the system. The evolution of the fault
indicators computed by the two previous approaches were
compared throughout this period of time. All of the indicators
behaved similarly and showed the development of an inner
bearing fault in the main bearing. This demonstrated that a
mechanical fault occurring in a drivetrain can be monitored
and detected by analysing electrical quantities, even if the
fault is located some distance from the electrical generator.
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