Structural Health and Condition Monitoring with Acoustic Emission and Guided Ultrasonic Waves: What about Long-Term Durability of Sensors, Sensor Coupling and Measurement Chain?

Acoustic Emission (AE) and Guided Ultrasonic Waves (GUWs) are non-destructive testing (NDT) methods in several industrial sectors for, e.g., proof testing and periodic inspection of pressure vessels, storage tanks, pipes or pipelines and leak or corrosion detection. In materials research, AE and GUW are useful for characterizing damage accumulation and microscopic damage mechanisms. AE and GUW also show potential for long-term Structural Health and Condition Monitoring (SHM and CM). With increasing computational power, even online monitoring of industrial manufacturing processes has become feasible. Combined with Artificial Intelligence (AI) for analysis this may soon allow for efficient, automated online process control. AI also plays a role in predictive maintenance and cost optimization. Long-term SHM, CM and process control require sensor integration together with data acquisition equipment and possibly data analysis. This raises the question of the long-term durability of all components of the measurement system. So far, only scant quantitative data are available. This paper presents and discusses selected aspects of the long-term durability of sensor behavior, sensor coupling and measurement hardware and software. The aim is to identify research and development needs for reliable, cost-effective, long-term SHM and CM with AE and GUW under combined mechanical and environmental service loads.

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Structural health monitoring of composite pressure vessels using guided ultrasonic waves

Insight - Non-Destructive Testing and Condition Monitoring ◽

10.1784/insi.2018.60.3.139 ◽

2018 ◽

Vol 60 (3) ◽

pp. 139-144 ◽

Cited By ~ 3

Author(s):

Y Lugovtsova ◽

J Prager

Keyword(s):

Structural Health Monitoring ◽

Health Monitoring ◽

Pressure Vessels ◽

Ultrasonic Waves ◽

Structural Health ◽

Guided Ultrasonic Waves ◽

Composite Pressure Vessels

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Pattern recognition approach for acoustic emission burst detection in a gearbox under different operating conditions

Journal of Vibration and Control ◽

10.1177/1077546319852536 ◽

2019 ◽

Vol 25 (17) ◽

pp. 2295-2304

Author(s):

Félix Leaman ◽

Cristián Molina Vicuña ◽

Ralph Baltes ◽

Elisabeth Clausen

Keyword(s):

Pattern Recognition ◽

Acoustic Emission ◽

Condition Monitoring ◽

Rotational Speed ◽

Operating Conditions ◽

Burst Detection ◽

Threshold Method ◽

Planetary Gearbox ◽

Industrial Sectors ◽

Artificial Neural Network Ann

Diverse machines in the mining, energy, and other industrial sectors are subject to variable operating conditions (OCs) such as rotational speed and load. Therefore, the condition monitoring techniques must be adapted to face this scenario. Within these techniques, the acoustic emission (AE) technology has been successfully used as a technique for condition monitoring of components such as gears and bearings. An AE analysis involves the detection of transients within the signals, which are called AE bursts. Traditional methods for AE burst detection are based on the definition of threshold values. When the machine under study works under variable rotational speed and load, threshold-based methods could produce inadequate results due to the influence of these OCs on the AE. This paper presents a novel burst detection method based on pattern recognition using an artificial neural network (ANN) for classification. The results of the method were compared to an adaptive threshold method. Experimental data were measured in a planetary gearbox test rig under different OCs. The results showed that both methods perform similarly when signals measured under constant OCs are considered. However, when signals are measured under different OCs, the ANN method performs better. Thus, the comparative analysis showed the good potential of the approach to improve an AE analysis of variable speed and/or load machines.

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