This book focuses on structural health monitoring in the context of machine learning. The authors review the technical literature and include case studies. Chapters include: operational evaluation, sensing and data acquisition, introduction to probability and statistics, machine learning and statistical pattern recognition, and data prognosis.

Structural Health Monitoring: A Machine Learning Perspective

Based on the extensive literature that has developed on structural health monitoring over the last 20 years, it can be argued that this field has matured to the point where several fundamental axioms, or gen eral principles, have emerged. The intention of this paper is to explicitly state and justify these axioms. In so doing, it is hoped that two subsequent goals are facilitated. First, the statement of such axioms will give new researchers in the field a starting point that alleviates the need to review the vast amounts of literature in this field. Second, the authors hope to stimulate discussion and thought within the community regarding these axioms.

The fundamental axioms of structural health monitoring

The components which often fail in a rolling element bearing are the outer-race, the inner-race, the rollers, and the cage. Such failures generate a series of impact vibrations in short time intervals, which occur at Bearing Characteristic Frequencies (BCF). Since BCF contain very little energy, and are usually overwhelmed by noise and higher levels of macro-structural vibrations, they are difficult to find in their frequency spectra when using the common technique of Fast Fourier Transforms (FFT). Therefore, Envelope Detection (ED) is always used with FFT to identify faults occurring at the BCF. However, the computation of ED is complicated, and requires expensive equipment and experienced operators to process. This, coupled with the incapacity of FFT to detect nonstationary signals, makes wavelet analysis a popular alternative for machine fault diagnosis. Wavelet analysis provides multi-resolution in time-frequency distribution for easier detection of abnormal vibration signals. From the results of extensive experiments performed in a series of motor-pump driven systems, the methods of wavelet analysis and FFT with ED are proven to be efficient in detecting some types of bearing faults. Since wavelet analysis can detect both periodic and nonperiodic signals, it allows the machine operator to more easily detect the remaining types of bearing faults which are impossible by the method of FFT with ED. Hence, wavelet analysis is a better fault diagnostic tool for the practice in maintenance.

Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities

This work motivates the need for more flexible structural similarity measures between time-series sequences, which are based on the extraction of important periodic features. Specifically, we present non-parametric methods for accurate periodicity detection and we introduce new periodic distance measures for time-series sequences. The goal of these tools and techniques are to assist in detecting, monitoring and visualizing structural periodic changes. It is our belief that these methods can be directly applicable in the manufacturing industry for preventive maintenance and in the medical sciences for accurate classification and anomaly detection.

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On Periodicity Detection and Structural Periodic Similarity

This paper deals with new procedures based on vibro-acoustic techniques to diagnose power medium-voltage/low-voltage (MV/LV) transformers. The normal operating machines can be monitored and checked from a remote, dedicated diagnostic center where information is received directly from the field. The diagnostic method adopted and the experimental test results are reported. Tests have been performed either in the laboratory or directly in the field on transformers in normal operating conditions. The laboratory tests related the transformer vibrations to internal anomalies. The suggested monitoring and diagnostic system allows one to usefully schedule preventative maintenance, reduce costs, and improve the quality of power distribution.

Vibro-acoustic techniques to diagnose power transformers

Vibration measurement transducers signal processing condition monitoring measurements instrumentation for recording and analysis mechanical condition measurements type, processing and display analyzing machine condition determining machine condition judging overall condition condition monitoring balancing alignment machinery analysis condition monitoring and predictive maintenance establishing a predictive monitoring program.

John S. Mitchell

Papers

An introduction to machinery analysis and monitoring