Ensemble learning‐based structural health monitoring by Mahalanobis distance metrics

Condition monitoring is an indispensable element related to the operation of rotating machinery. In this article, the monitoring system for the parallel gearbox was proposed. The novelty detection approach is used to develop the condition assessment support system, which requires data collection for a healthy structure. The measured signals were processed to extract quantitative indicators sensitive to the type of damage occurring in this type of structure. The indicator’s values were used for the development of four different novelty detection algorithms. Presented novelty detection models operate on three principles: feature space distance, probability distribution, and input reconstruction. One of the distance-based models is adaptive, adjusting to new data flowing in the form of a stream. The authors test the developed algorithms on experimental and simulation data with a similar distribution, using the training set consisting mainly of samples generated by the simulator. Presented in the article results demonstrate the effectiveness of the trained models on both data sets.

Section: Novelty Detection In Condition Monitoringmentioning

confidence: 99%

Comparison of Novelty Detection Methods for Detection of Various Rotary Machinery Faults

Górski

Jabłoński

Heesch

et al. 2021

“…To this aim, the most relevant techniques are statistical distance measures, which may depend upon the type of damage-sensitive features to handle. Some of the useful univariate and multivariate distance techniques to mention include the Mahalanobis distance [ 20 , 21 , 22 ] and Kullback–Leibler divergence [ 15 , 23 , 24 ], dynamic time warping [ 25 ], and other damage indices based on relative errors [ 26 , 27 ], classical and robust multidimensional scaling algorithms [ 28 , 29 ], etc.…”

Section: Introductionmentioning

confidence: 99%

“…This issue becomes even worse when the limited information is coupled with the environmental and/or operational variability conditions. These are deceptive effects, such as temperature fluctuations, humidity and moisture variations, wind speed, human movements, and traffic, that provide changes similar to damage in the sensed structural response and lead to an outlier masking problem [ 21 ]. In such cases, false alarms and erroneous detection results present the major challenges [ 20 , 38 , 39 ].…”

Section: Introductionmentioning

confidence: 99%

Damage Detection in Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology

Entezami

Mariani

Shariatmadar

2022

Self Cite

Vibration-based damage detection in civil structures using data-driven methods requires sufficient vibration responses acquired with a sensor network. Due to technical and economic reasons, it is not always possible to deploy a large number of sensors. This limitation may lead to partial information being handled for damage detection purposes, under environmental variability. To address this challenge, this article proposes an innovative multi-level machine learning method by employing the autoregressive spectrum as the main damage-sensitive feature. The proposed method consists of three levels: (i) distance calculation by the log-spectral distance, to increase damage detectability and generate distance-based training and test samples; (ii) feature normalization by an improved factor analysis, to remove environmental variations; and (iii) decision-making for damage localization by means of the Jensen–Shannon divergence. The major contributions of this research are represented by the development of the aforementioned multi-level machine learning method, and by the proposal of the new factor analysis for feature normalization. Limited vibration datasets relevant to a truss structure and consisting of acceleration time histories induced by shaker excitation in a passive system, have been used to validate the proposed method and to compare it with alternate, state-of-the-art strategies.

“…Once damage-sensitive features have been extracted from the dataset, the final step of a data-driven SHM method is to analyze the features themselves for decision-making, providing outcomes in terms of early damage detection, localization, and quantification. At this stage, different techniques can be adopted, including statistical distance metrics (e.g., the Mahalanobis distance [ 23 , 24 ] or the Kullback–Leibler divergence [ 10 , 21 , 25 ]), Bayesian approaches [ 26 , 27 ], artificial neural networks [ 28 , 29 ], principal component analysis [ 30 , 31 ], and clustering [ 32 , 33 , 34 ]. In spite of their applicability, they may not perform efficiently when damage-sensitive features are of a high-dimensional nature, namely in the presence of big data to process; this leads to a time-consuming and unreliable decision-making process [ 10 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

Health Monitoring of Large-Scale Civil Structures: An Approach Based on Data Partitioning and Classical Multidimensional Scaling

Entezami

Sarmadi

Behkamal

et al. 2021

Self Cite

A major challenge in structural health monitoring (SHM) is the efficient handling of big data, namely of high-dimensional datasets, when damage detection under environmental variability is being assessed. To address this issue, a novel data-driven approach to early damage detection is proposed here. The approach is based on an efficient partitioning of the dataset, gathering the sensor recordings, and on classical multidimensional scaling (CMDS). The partitioning procedure aims at moving towards a low-dimensional feature space; the CMDS algorithm is instead exploited to set the coordinates in the mentioned low-dimensional space, and define damage indices through norms of the said coordinates. The proposed approach is shown to efficiently and robustly address the challenges linked to high-dimensional datasets and environmental variability. Results related to two large-scale test cases are reported: the ASCE structure, and the Z24 bridge. A high sensitivity to damage and a limited (if any) number of false alarms and false detections are reported, testifying the efficacy of the proposed data-driven approach.