Optimal Preventive Maintenance of Wind Turbine Components with Imperfect Continuous Condition Monitoring

Abstract

Among the different maintenance techniques applied to wind turbine (WT) components, online condition monitoring is probably the most promising technique. The maintenance models based on online condition monitoring have been examined in many studies. However, no study has considered preventive maintenance models with incorporated probabilities of correct and incorrect decisions made during continuous condition monitoring. This article presents a mathematical model of preventive maintenance, with imperfect continuous condition monitoring of the WT components. For the first time, the article introduces generalized expressions for calculating the interval probabilities of false positive, true positive, false negative, and true negative when continuously monitoring the condition of a WT component. Mathematical equations that allow for calculating the expected cost of maintenance per unit of time and the average lifetime maintenance cost are derived for an arbitrary distribution of time to degradation failure. A numerical example of WT blades maintenance illustrates that preventive maintenance with online condition monitoring reduces the average lifetime maintenance cost by 11.8 times, as compared to corrective maintenance, and by at least 4.2 and 2.6 times, compared with predetermined preventive maintenance for low and high crack initiation rates, respectively.

A Historical Survey of Corrective and Preventive Maintenance Models with Imperfect Inspections: Cases of Constant and Non-Constant Probabilities of Decision Making

“…In 2019, ref. [102] proposed a mathematical framework for the preventive maintenance of wind turbine components with imperfect continuous condition monitoring in the interval (0, τ). The decision to perform maintenance on the wind turbine component relies on data obtained from the sensors.…”

“…The expected maintenance cost per unit time is calculated by dividing the average maintenance cost for the regeneration cycle by its average duration [102].…”

Effects of imperfect IoT-enabled diagnostics on maintenance services: A system design perspective

“…Second, operational management of remote monitoring and diagnostics has attracted research attention in recent years (Guillén et al 2016;Raza and Ulansky 2019;Raja et al 2017). Kurz (2016) and Kurz and Pibernik (2016) analyzed the effects of advanced information created and collected by real time condition monitoring for maintenance services.…”

“…However, the company also faces imperfect prediction signals such as false positives and false negatives (Topan et al 2018). Another example of this problem is the maintenance services for wind turbines installed worldwide (Raza and Ulansky 2019). This imperfect monitoring considerably influences maintenance services (Topan et al 2018;Nguyen et al 2019); however, few studies have investigated the problem.…”

Predicting Frequency, Time-To-Repair and Costs of Wind Turbine Failures

“…They concluded that the optimum scheduled maintenance interval should be 113 days instead of a typical industry schedule of 180 days, whereas Kerres et al (2014) [11] found that corrective maintenance done upon a failure, is a better option for a V44-600 kW turbine. Raza and Ulansky (2019) [12] applied imperfect continuous condition monitoring which considers correct and incorrect decisions about preventive maintenance for wind turbines. They concluded that a preventive maintenance could reduce the average lifetime maintenance cost 11.8 times comparing the corrective maintenance for wind turbine blades.Overall, several of the previous studies diverse in their findings and new approaches to data analysis and modeling may be needed to provide more cohesive guidance to wind turbine O&M managers.…”