Vibration Signal Evaluation Based on K-Means Clustering as a Pre-Stage of Operational Modal Analysis for Structural Health Monitoring of Rotating Machines

Abstract

Rotating machines are key components in energy generation processes, and faults can lead to shutdowns or catastrophes encompassing economic and social losses. Structural Health Monitoring (SHM) of structures in operation is successfully performed via Operational Modal Analysis (OMA), which has advantages over traditional methods. In OMA, white noise inputs lead to the accurate extraction of modal parameters without taking the system out of operation. However, this excitation condition is not easy to attain for rotating machines used in power generation, and OMA can provide inaccurate information. This research investigates the applicability of machine learning as a pre-stage of OMA to differentiate adequate from inadequate excitations and prevent inaccurate extraction of modal parameters. Data from a rotor system was collected under different conditions and OMA was applied. In a training stage, measurements were characterized by statistical features and K-means was used to determine which features provided information about the excitation condition, that is, which excitation was adequate to extract the rotor’s modal parameters via OMA. In a testing stage, data were successfully classified as adequate or not adequate for OMA, achieving 100% accuracy and revealing the technique’s potential to support SHM of rotating machines. The technique is extendable to other monitoring systems based on OMA.

AdaBoost Ensemble Approach with Weak Classifiers for Gear Fault Diagnosis and Prognosis in DC Motors

“…where C is the set of clusters, C i is the ith cluster, µ i is the centroid of cluster C i , and || • || denotes the Euclidean distance. K-Means clustering is explored as a precursor to Operational Modal Analysis (OMA) in monitoring rotating machines used in energy generation [22]. By distinguishing suitable excitation conditions through statistical features, this approach enhances OMA accuracy, ensuring effective Structural Health Monitoring (SHM) and potentially preventing shutdowns or catastrophic failures, thereby safeguarding economic and social interests [22].…”

“…The ensemble classifier H(x) can be calculated using Equation (22), aggregating the predictions of all weak classifiers h t weighted by their respective weights α t , and producing the final prediction. The model parameter calculation is crucial to the proposed methodology for fault diagnosis and prognosis in machine health monitoring:…”