*1.1. Research Background*

Improving the operating efficiency of wind turbines and reducing maintenance costs are goals common to all wind farm operators. However, similar to the erosional effects caused by seawater and weather conditions on the base constructs of offshore or onshore wind turbines, machines and equipment are also prone to failure, leading to a loss of efficiency in power generation operations. However, an integrated perspective to examine these issues is seldom discussed [1–3].

The industry developing wind turbines has adopted a conservative attitude, i.e., it lacks integration with related upstream and downstream industries. In some countries, for example, blade manufacturers concentrate only on the R&D (research and development) of the blade shape and materials, while the generator manufacturers care only about the design and manufacture of the generator bodies. The power-converter manufacturers alone bear the final responsibility for power generation efficiency. However, converter manufacturers do not usually have the expertise to integrate blades and generators. Moreover, the international market currently lacks protocols or standard operating procedures (SOPs) for these integrative matters. This is critical for turbine maintenance.

**Citation:** Hsu, M.-H.; Zhuang, Z.-Y. An Intelligent Detection Logic for Fan-Blade Damage to Wind Turbines Based on Mounted-Accelerometer Data. *Buildings* **2022**, *12*, 1588. https://doi.org/10.3390/ buildings12101588

Academic Editor: Zhenjun Ma

Received: 17 August 2022 Accepted: 28 September 2022 Published: 1 October 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Although avoiding the replacement of faulty major parts is the most efficient way to reduce operating costs, an alternative logic for turbine maintenance entails detecting faults efficiently and replacing the necessary part promptly, in addition to scheduled preventive maintenance (e.g., predictive maintenance). Within the entire power generation system utilising wind as a resource, the blade is an important component. To ensure the normal operation of a wind turbine, the key factors are the durability, safety and reliability of the wind turbine blades, and these have become a concern for wind farm operators and manufacturers. However, to the authors' knowledge, most fan blade health diagnoses today still rely on subjective human efforts to identify whether the blades are damaged or not, i.e., acoustic or vision-based inspections performed by professionals, and currently there is no rapid and objective method for blade diagnosis [1–3].

Benbouzid et al. [4] reviewed the current progress in condition monitoring of wind turbines, from traditional condition monitoring and signal-processing tools to machine learning-based condition monitoring and predictive maintenance using big-data mining. Their systematic review of signal-based and data-driven modelling approaches using intelligence and machine learning approaches examined recent developments in the field and their use in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms [4]. Natilli et al. [5] developed a multi-scale method for wind turbine gearbox fault detection and tested it in real-world test cases. The novelty of this work is that the detection method was developed using industrial datasets provided by standard SCADA (supervisory control and data acquisition) and TCM (turbine condition monitoring) systems [5].

Papi [6] proposed using an uncertainty-quantification method to model the effect of blade damage on the performance of multi-megawatt wind turbines. The proposed method aims to overcome some of the problems associated with evaluating individual test cases. In fact, treating blade damage as a random phenomenon avoids biases due to specific test cases of combinations of blade damage factors and allows for more general conclusions [6]. This point is critical for this study. Santolamazza et al. [7] presented an approach based on machine learning techniques using data from SCADA systems. Since these systems are usually already implemented on most wind turbines, they provide a lot of data without the need for additional sensors. In particular, they used artificial neural networks (ANNs) to develop models to describe the behaviour of some of the main components of wind turbines, such as gearboxes and generators, and predict operational anomalies. The proposed method was tested on real wind turbines in Italy to verify its effectiveness and applicability and proved to be able to provide important assistance in the maintenance of wind farms [7].

Knowing the status of the blades at any time should be critical to the normal operation of turbines at the lowest cost, because wind has become one of the most popular sources of green power generation (solar being another popular energy source), and more and more wind farms are operating or being constructed worldwide. The offshore equipment is usually expensive and produces electricity almost constantly; however, it may fail, incurring significant operational losses if it cannot be repaired quickly during the downtime [1–3].

The repair and subsequent maintenance tasks involve considerable effort and costs, thus exacerbating the problem. Moreover, in some places, such as Penghu (or the Pescadores Islands) and Taiwan, most wind turbines are maintained by non-native manufacturers, who regard internal equipment maintenance as a business secret (and do not disclose it easily). However, a discussion about this critical problem is beyond the scope of this paper. In any case, there is a need to detect and diagnose problems related to offshore wind turbines quickly, in order to launch the subsequent unplanned yet necessary repairs [1–3].

#### *1.2. This Study: An Overview*

In this study, a complete functioning mechanical condition monitoring system (CMS) is established with an initial stable performance, which measures parameters such as the vibrations and noises of a turbine during operation. The recorded data are stored and reported remotely (from the remote-side CMS). They are organised as datasets (on the server-side CMS), and the characteristics and patterns of/in the signal are then analysed using the established rules and classified as having different levels of fan-blade damage severity. This process automatically detects the health status of a turbine by inspecting its vibration states, rather than through human efforts.

Thus, if effective rules can be established to program the CMS (which embeds these rules) to detect the different severity levels of turbine fan-blade damage based on a real-time database that is regularly imported and updated by the (remote) CMS, the performance of the server-side CMS relies heavily on the effectiveness of the rules. Once an anomaly is detected, the CMS serves to guide the subsequent fan maintenance and repair actions. In this study, we call this 'maintenance by prediction', in contrast to 'predictive maintenance', which is a type of 'preventive maintenance' [1–3].

The CMS can thus warn of blade damage almost in real time, allowing wind farm operators to launch and arrange repair operations for maintenance by prediction at the earliest moment. This reduces the turbine's downtime. As such, this is different from predictive maintenance, which aims to avoid wasting time and money by preventing serious damage by performing regular maintenance in advance. Maintenance by prediction operates by giving sudden fan-blade status warnings (in the event of damage) for launching the actions as soon as possible.

An example may help to illustrate the difference. During predictive maintenance, the engineers are recommended to replace some parts of the turbine, but they may be replaced improperly, ultimately resulting in a machine shutdown. By contrast, the proposed maintenance by prediction mechanism works to detect, warn of, and support addressing an incident in almost real time.

Theoretically, the mechanism proposed by this study to detect wind turbine blade damage in wind turbines aims to support unplanned maintenance and repairs, which are carried out for the faulty wind turbine(s) using rule-based predictions (i.e., the maintenance by prediction actions). If the CMS is programmed with this detection mechanism, most of the equipment manager's efforts can be focused on the machine's normal operations, e.g., acquiring or preparing the parts required for equipment repairs, i.e., preventive maintenance. In operational practice, such a CMS can improve the safety (effective operation) rate (or operating efficiency) of the power system, reduce maintenance costs and increase the reliability of the wind turbine [1–3,8–12].

Section 2 focuses on the design of the CMS for wind turbines, describing how our research led us to the fault-diagnosis functions for fan blades from the traditional functions used for power generators, while reviewing the relevant literature. Section 3 presents the results from the data analysis upon taking real sampling data and introduces the methods used 'by example' (on the fly). Although the methods being applied are individually common in data processing, statistics, and digital signal processing (DSP), hybridising them for the purpose of the application described in this study is novel. The rules are established in Section 4 based on the analytical results, followed by a discussion of key findings. Section 5 concludes this study.
