**5. Conclusions**

Green energy has become a major power source over the past two decades. Wind does not pollute and is currently one of the most promising clean and inexhaustible energy sources for power generation [24]. Recent advances in wind energy production have helped to solve practical problems [25] and improve quality of life [26]. Due to the torsional and flexural coupling of the pre-bent blades, the dynamic characteristics of blades made from orthotropic composite materials with conical pre-torsion are much more complex than those of isotropic blades. On the other hand, this means that—compared to other components of a wind turbine whose designs have had time to mature—the relevant technologies and designs for the fan blades are relatively new.

Condition monitoring and fault diagnosis of wind turbines have gained more and more practical value for reducing maintenance costs and improving wind farms' operational efficiency [27], because more and more wind farms are being constructed and operated globally. Thus, the market has become competitive, and wind farm operators usually need to reduce their operating and maintenance costs in order to make their operations more profitable, and to maintain the sustainable competitive advantage (SCA) of the company, the maintenance strategies must be effective as operations continue. Due to these drivers, the condition monitoring and early fault diagnosis of wind turbines have become required industrial practices because they help improve the reliability and productivity of wind farms [28].

Due to the high maintenance costs (and efforts) incurred, the failure of wind turbine blades during the operation and maintenance phases has become a major problem for the wind power industry. Therefore, the utilisation of quality real-time data [29] and the development of methods to monitor the turbine blades' integrity is critical [30] because of the novelty of the blades' designs (which also determine the feature(s) of some turbine types of certain turbine brands).

In order to detect blade damage, after a review, we found that vibrations and noises are the two interfaces through which to determine mechanical faults, and the signals these carry can be analysed to determine the cause of the event. However, after long laboratory trials, we also found that taking these signals and using the existing justification criteria from research on power generators is infeasible and ineffective for diagnosing fan-blade faults using CMS with the wind turbine on the remote side. The rules (criteria) for detecting faulty situations therefore had to be reconsidered, and the idea of detecting faults using remote-side CMS with limited computation power had to be abandoned.

This led to the idea to establish effective new rules for the server-side CMS to detect faulty situations based on the vibration data transmitted and updated from the remote-side CMS, because using the datasets recorded from an interface were sufficient and using the interface recording vibrations would be better. This resulted in the creation of a new plug-in for the failure and cause-of-failure analysis module on the server-side CMS, which detects sudden faulty events and types of fan-blade damage in almost real time (see Figure 1). It can then send an alert message via SMS or email to the engineers on duty, so that they can make repairs immediately.

In this sense, it may help to establish a maintenance by prediction mechanism for unplanned maintenance when any fan blade is out of order, which supplements the common planned tasks carried out for the preventive maintenance of the fan blades. Despite the suggested mechanism playing a supplementary role to regular preventive maintenance being the main possible contribution, it should be particularly noted that this point is also exactly the boundary of this study, i.e., no work related to traditional preventive maintenance is presented. Another boundary of the study should be that the mechanism suggested by this study is for turbines having blades made of isotropic materials (because of current experimental limitations), so there is still room for exploring whether or not it still holds (or is there any other more effective mechanism) for those turbines having orthotropic blades.

All of this relies on the effective rules established to identify the 3.0-blade (full blade or normal), 2.5-blade (half or part of the blade broken) and 2.0-blade (one blade totally missing) cases while the turbines are operating. Fortunately, with the help of a contemporary datadriven approach and the adoption of suitable data processing/analysis methods in both DSP and statistics, we found that watching a continuous (but short, in terms of sampling time) sequence of the vibration dataset was sufficient to establish these rules. Fortunately, despite the considerable time and effort put into the data experiments to determine the rules, these rules are not difficult to implement on any given CMS, because:

(1) No real domain transformation is required, and only data conversions happen: the rules work with the data in the source domain, with just a few conversions (rather than transformations) of the data required for the computation;

(2) The rules are data-pattern-based, rather than learning-based: fixed judgements are made on the observable patterns or characteristics in the converted data, so no other processes, such as training, verifying, or parametric tuning efforts, are needed; and

(3) The rules are simple: only two rules are included (and required) to make the judgements, and they are simple, so they can be designed as additional functions of the CMS without utilising significant run-time computational resources.

The real use of these rules on the server-side CMS is expected in the future, and their true value will be shown when the unplanned maintenance by prediction tasks is someday carried out for the turbine blades. We sincerely hope that in the future, these rules can benefit not only the company operating the wind farms but also the entire wind turbine industry. They have the potential to change the ways people approach CMS design for wind turbines, as the effectiveness of data-driven server-side fault diagnostics has been made evident in this study. Finally, while the proposed mechanism works for shoreside wind turbines, a similar logic can be generalised to other wind turbines (e.g., offshore or inland) as well.

**Author Contributions:** Conceptualization, funding acquisition, methodology, writing—original draft preparation, M.-H.H. and Z.-Y.Z.; data curation, software (data experiments), writing—review and editing, visualization, Z.-Y.Z.; formal analysis, investigation (physical experiments), validation, M.-H.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by National Science and Technology Council, Taiwan, ROC ('Ministry of Science and Technology' or 'MOST' before July 2022), under grants MOST-109-2221-E-346-001, MOST-110-2410-H-992-020 and MOST-111-2410-H-992-011.

**Data Availability Statement:** This research did not use publicly archived datasets.

**Conflicts of Interest:** The authors declare no conflict of interest.
