*2.3. Fault-Diagnosis Functions for Wind Turbines*

This subsection illustrates the fault-diagnosis process and the analysis functions of the CMS for a wind turbine. When the machine experiences abnormal vibration, the diagnostic analysis can be performed according to the following guidelines [13–19]:

(A) Understand the machine's construction. For an abnormally functioning machine, it is necessary to understand its primary structure, component transmission methods and even an overview of the system to determine the possible failure modes and the appropriate measurement method and position.

(B) Observe abnormalities. When an exception occurs in a machine, it is necessary to understand the machine's operational conditions at that moment and to observe the abnormal phenomenon that is causing the device to operate abnormally.

(C) Abnormal phenomenon measurement. It is necessary to select appropriate sensors and instrumentation equipment, measure the vibration signal of the unusual machine phenomenon, and process the measured signal through spectrum analysis.

(D) Abnormal signal analysis. The abnormal signals measured should be analysed to determine the most severe locations and conditions and the components that are causing the problems. The reasons for these problems should be investigated. If necessary, further measurements should be made to confirm the causes of the issues.

(E) Verification of the results. After confirming the cause (or causes) of the abnormality, and after repairing or replacing the machine's faulty components, it is necessary to observe or measure again to determine whether the abnormality still exists and whether there are any signs of improvement. If the situation has not improved, the above steps must be repeated until the problem is resolved.

The following list summarises the judging criteria for abnormal situations that have traditionally been applied for fault diagnosis on the remote side (or 'device side', 'client side', or 'site side') CMS for the power generator component(s). Usually, a subset of these criteria is sufficient to program the diagnostic function. Appendix A [1–3,17,18] presents detailed descriptions.


While the faults listed above are mainly mechanical problems that can be judged using vibration signals (with the means of judging these detailed in Appendix A), the acoustic interface is another main interface used to understand whether a wind turbine is faulty, specifically through noise measurements. Appendix B provides further details about this process [1–3,17,18]. Theoretically, then, the measurement results from either interface, or both, can be considered [1–3,13–19,21] on the remote side.

However, the following statements are critical for explaining the reasons this research is conducted recording only the vibration data (and not the noise data) on the 'remote side' of the CMS and utilising the collected datasets on the 'server side'.


From the above discussion, as using the vibration datasets is sufficient (and using these is better than using the noise datasets due to the causal relationship), and as the traditional criteria for diagnosing a power generator do not apply in diagnosing the component of turbine blades on the remote side, we decided to seek clues to establish the judgement rules from the vibration datasets received and gathered on the server side.

#### *2.4. Developing Wind Turbines: A Briefing*

To demonstrate the role of wind turbine blades in designing and maintaining a wind turbine, as a supplemental review, Figure 1 shows the life cycle of wind turbine development.

**Figure 1.** The life cycle of wind turbine development.

After a wind turbine is set up, if it becomes inoperable, most wind turbine manufacturers will assert that it was because the turbine was not properly maintained. Then, to argue with the manufacturer, the engineers in a green energy operator company would like to design a simulation program based on a given turbine manufacturer's model and run it to determine if the design was flawed. However, it is questionable whether or not the operator can successfully request the design drawings and related data for subsequent operations, maintenance, monitoring and analysis from the manufacturer in practice, let alone build up a simulated turbine. Wind turbine manufacturers believe that wind turbine design drawings and related technologies are trade secrets, so technology transfers are impossible for them; a chance of negotiation exists only if the business deal involves a large-scale wind farm. In reality, however, most cases do not contain this possibility, even in a large-scale wind farm construction project (to the authors' knowledge).

Therefore, based on the operation and maintenance phase in Figure 1, we must rely on the condition monitoring approach using a CMS (both remote side and server side) and perform the repair or replacement tasks (i.e., the unplanned maintenance outside of the regular, periodic maintenance tasks) when this system detects a sudden faulty status of the turbine while it is operating.

Blade design is the most commonly addressed topic in the design phase for wind turbines because it is usually used to distinguish the brand and type of the turbine directly (visually) and because the technologies for the design of all other components (generator, tower, convertor and controller) were mature long before wind turbines appeared. Thus, this study focuses on the detection of blades' sudden faulty status. Before this, it is necessary to seek clues for establishing the effective rules for the detection process based on the vibration data transmitted to the server side in near real time from the remote side.

An effective CMS providing accurate predictive detections and posing precise warnings for subsequent maintenance by prediction actions cannot proceed without these rules. Strictly speaking, the methods to establish these rules also fall within the scope of the failure and cause-of-failure analyses phase in Figure 1, in addition to the traditional tasks usually defined based upon the testing failures. The next section presents the results obtained from the data analysis, which forms the basis for establishing the rules.
