Using Abductive Machine Learning for Online Vibration Monitoring of Turbo Molecular Pumps

Shock and Vibration, Sep 2018

Turbo molecular vacuum pumps constitute a critical component in many accelerator installations, where failures can be costly in terms of both money and lost beam time. Catastrophic failures can be averted if prior warning is given through a continuous online monitoring scheme. This paper describes the use of modern machine learning techniques for online monitoring of the pump condition through the measurement and analysis of pump vibrations. Abductive machine learning is used for modeling the pump status as ‘good’ or ‘bad’ using both radial and axial vibration signals measured close to the pump bearing. Compared to other statistical methods and neural network techniques, this approach offers faster and highly automated model synthesis, requiring little or no user intervention. Normalized 50-channel spectra derived from the low frequency region (0–10 kHz) of the pump vibration spectra provided data inputs for model development. Models derived by training on only 10 observations predict the correct value of the logical pump status output with 100% accuracy for an evaluation population as large as 500 cases. Radial vibration signals lead to simpler models and smaller errors in the computed value of the status output. Performance is comparable with literature data on a similar diagnosis scheme for compressor valves using neural networks.

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Using Abductive Machine Learning for Online Vibration Monitoring of Turbo Molecular Pumps

Shock and Vibration 1070-9622 Using abductive machine learning for online vibration monitoring of turbo molecular pumps R.E. Abdel-Aal 0 M. Raashid 0 0 Center for Applied Physical Sciences, Research Institute, King Fahd University of Petroleum and Minerals , Dhahran 31261 , Saudi Arabia - Turbo molecular vacuum pumps constitute a critical component in many accelerator installations, where failures can be costly in terms of both money and lost beam time. Catastrophic failures can be averted if prior warning is given through a continuous online monitoring scheme. This paper describes the use of modern machine learning techniques for online monitoring of the pump condition through the measurement and analysis of pump vibrations. Abductive machine learning is used for modeling the pump status as ‘good’ or ‘bad’ using both radial and axial vibration signals measured close to the pump bearing. Compared to other statistical methods and neural network techniques, this approach offers faster and highly automated model synthesis, requiring little or no user intervention. Normalized 50-channel spectra derived from the low frequency region (0–10 kHz) of the pump vibration spectra provided data inputs for model development. Models derived by training on only 10 observations predict the correct value of the logical pump status output with 100% accuracy for an evaluation population as large as 500 cases. Radial vibration signals lead to simpler models and smaller errors in the computed value of the status output. Performance is comparable with literature data on a similar diagnosis scheme for compressor valves using neural networks. Keywords: Vibration monitoring and diagnostics, statistical vibration analysis, turbo molecular pumps, machine learning, abductive networks 1. Introduction The 350 kV light ion accelerator facility [ 4 ] at King Fahd University of Petroleum and Minerals (KFUPM) employs some 15 Balzers turbo molecular vacuum pumps of various capacities to achieve a minimum vacuum level of 1:33 10 4 Pa. Table 1 gives a summary of the specifications and operating conditions for a typical 0.5 m3/s pump, model TPU 510, and its electronic drive unit model TPC 300. Many of the pumps run continuously for extended periods, and operational experience has shown that bearing failures while the pump is running at full speed can completely destroy the pump. Such failures often occur without adequate warning signs that can be detected through routine manual inspection. Even in cases when there is a change in the pump noise, this may go unnoticed in the noisy environment of the accelerator vault or may occur after normal working hours when the facility is left unattended. A turbo pump is an expensive piece of equipment, and pump failures can also be costly in terms of lost beam time if there is a need to wait for in-house repair or for a replacement pump to be ordered from abroad. We have recently initiated work on the development of an online monitoring scheme for the accelerator pumps with the objective of automatically detecting abnormalities in the pump condition and warning the accelerator operator in advance to avert serious failures. The importance of continuous online monitoring for critical machinery is well established [ 21 ], since monthly or weekly manual measurements may not be frequent enough or consistent enough to detect developing problems. Vibration analysis truth tables have been used for many years as a guide for diagnosing vibrations in rotating machinery, but conclusive results often require further evidence [ 24 ]. Recent advances in computers, instrumentation, and signal processing techniques have made online predictive vibration monitoring of machinery available and cost-effective approach in many situations [ 21 ]. Techniques used include time domain and frequency domain analysis as well as combinations of both. Univariate time series analysis [ 29 ] and multivariate linear regression methods [ 19 ] have been employed to model normal vibration behavior in the time domain. Problems with the first approach include strong nonstationarity of the vibration time series, as in the case of reciprocating machinery. The second technique suffers from difficulties in determining suitable relevant time series that explain variations in the vibration data, as well as strong correlations between the various input time series. The two techniques require complex computations and considerable user intervention for each analysis performed, which makes them difficult to implement online using simple portable apparatus. Frequency domain techniques use the frequency spectrum of the vibration signal as a signature for the pump condition, e.g. [ 8 ]. A recent trend in many areas of applied sciences has been to resort to a machine learning approach when a rigorous algorithmic solution becomes too complex or when the underlying relationships between inputs and outputs are not known. With this approach, a (...truncated)


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R.E. Abdel-Aal, M. Raashid. Using Abductive Machine Learning for Online Vibration Monitoring of Turbo Molecular Pumps, Shock and Vibration, 6, DOI: 10.1155/1999/560297