The accuracy and precision of diagnostic features in a Prognostics and Health Management (PHM) system depends on the feature's sensitivity to not only signal quality or signal-to-noise ratio (SNR), but also failure modes and operating conditions. The data acquired in real applications are not only a measure of the direct response of the system of interest, but also unwanted noises or abnormal signals. Before extracting diagnostic features, it is therefore critical to reduce the noises and eliminate the abnormal data (i.e., outliers) that can contaminate the data and result in a distortion or reduction in the amount of accurate information that can be obtained. In this paper, the authors will review existing outlier detection methods, such as Distance Based (DB), DBSCAN, and Minimum Covariance Determinant (MCD), and highlight their application to identify the data points that should be discarded. While these outlier detection methods will detect and help remove outliers, there is still a need to eliminate or reduce random noise or unwanted signals. Therefore, the authors have also applied a number of noise reduction techniques to the raw signal to increase the SNR. Finally, the on-board sensors mounted on a system operating in extreme environments can experience dramatic shifts in outputs over short durations or transient events. Furthermore, most existing diagnostic features are quite sensitive to the system's operating conditions (e.g., speed, load, or torque) and therefore directly using the features for the system health monitoring can cause erroneous results or high false alarm rates. To mitigate the influence of transient data, the authors employ a clustering method for operational mode detection, as well as a novel feature normalization technique on the operating conditions. In this paper, the authors will compare and evaluate the contributions of the afore-mentioned algorithms and techniques to rotating machinery health monitoring. 1, 2
[1]
David L. Woodruff,et al.
Identification of Outliers in Multivariate Data
,
1996
.
[2]
H. Kriegel,et al.
Spatial Data Mining: Database Primitives, Algorithms and Efficient DBMS Support
,
2000,
Data Mining and Knowledge Discovery.
[3]
Matthew J. Watson,et al.
A Comprehensive High Frequency Vibration Monitoring System for Incipient Fault Detection and Isolation of Gears, Bearings and Shafts/Couplings in Turbine Engines and Accessories
,
2007
.
[4]
Raymond T. Ng,et al.
Algorithms for Mining Distance-Based Outliers in Large Datasets
,
1998,
VLDB.
[5]
David L. Donoho,et al.
De-noising by soft-thresholding
,
1995,
IEEE Trans. Inf. Theory.
[6]
James N Caron,et al.
Blind deconvolution of audio-frequency signals using the self-deconvolving data restoration algorithm.
,
2004,
The Journal of the Acoustical Society of America.
[7]
S. Haykin,et al.
Adaptive Filter Theory
,
1986
.
[8]
Carl S. Byington,et al.
High Frequency Incipient Fault Detection for Engine Bearing Components
,
2005
.
[9]
Philip D. Wasserman,et al.
Advanced methods in neural computing
,
1993,
VNR computer library.
[10]
Hans-Peter Kriegel,et al.
A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise
,
1996,
KDD.