Model-based fault detection and diagnosis in ALMA subsystems

Ortiz, J. L.; Carrasco, Rodrigo A.

doi:10.1117/12.2233204

Cited by 10 publications

(13 citation statements)

References 21 publications

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“…To measure our scheme's effectiveness, we compared the results of our system with a Ground Truth. This ground truth was constructed from the original analysis in [8], later validated by the engineering team at ALMA by manually revising the maintenance logs, to determine the actual date when the IFP was detected to be faulty by a human operator. A summary of all the analyzed cases is shown in Appendix A.…”

Section: Implementation and Resultsmentioning

confidence: 99%

“…We also compared our scheme with the one currently in use by ALMA, and detailed in [8]. Figure 12 shows a histogram with the days gained by the ESN-based scheme.…”

Section: Implementation and Resultsmentioning

confidence: 99%

“…Figure 12 shows a histogram with the days gained by the ESN-based scheme. Although the reaction time is almost the same (in average 0.7 days slower), the scheme presented in [8] required that the maintenance team to determine the system's dynamics FIGURE 12. Histogram of the number of days the ESN detects a fault before the FDD Scheme in [8].…”

Section: Implementation and Resultsmentioning

confidence: 99%

“…They also test the effectiveness of this approach and show that it is possible to achieve better detection by using more than one residual. In an application to modern telescopes, Ortiz and Carrasco [8], [9] developed a framework of fault detection and diagnosis, using a bank of Kalman filters to detect a specific type of slow degradation faults. They tested their scheme with real fault data from ALMA, showing excellent accuracy.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…The Intermediate Frequency Processor (IFP) of the antennas of the ALMA telescope, as described in [8], is a critical component responsible for the second down-conversion, signal filtering, and amplification of the total power measurement of sidebands and basebands. This subsystem allows for effective communication of the captured data to the central correlator for processing, thus making it a central and critical component of each antenna.…”

Section: A Problem Descriptionmentioning

confidence: 99%

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Slow Degradation Fault Detection in a Harsh Environment

et al. 2020

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The ever increasing challenges posed by the science projects in astronomy have skyrocketed the complexity of the new generation telescopes. Due to the climate and sky requirements, these highprecision instruments are generally located in remote areas, suffering from the harsh environments around it. These modern telescopes not only produce massive amounts of scientific data, but they also generate an enormous amount of operational information. The Atacama Large Millimeter/submillimeter Array (ALMA) is one of these unique instruments, generating more than 50 Gb of operational data every day while functioning in conditions of extreme dryness and altitude. To maintain the array working under extreme conditions, the engineering teams must check over 130,000 monitoring points, combing through the massive datasets produced every day. To make this possible, predictive tools are needed to identify, hopefully beforehand, the occurrence of failures in all the different subsystems. This work presents a novel fault detection scheme for one of these subsystems, the Intermediate Frequency Processors (IFP). This subsystem is critical to process the information gathered by each antenna and communicate it, reliably, to the correlator for processing. Our approach is based on echo state networks, a configuration of artificial neural networks, used to learn and predict the signal patterns. These patterns are later compared to the actual signal, to identify failure modes. Additional preprocessing techniques were also added since the signal-to-noise ratio of the data used was very low. The proposed scheme was tested in over seven years of data from 132 IFPs at ALMA, showing an accuracy of over 70%. Furthermore, the detection was done several months earlier, on average, when compared to what human operators did. These results help the maintenance procedures, increasing reliability while reducing humans' exposure to the harsh environment where the antennas are. Although applied to a specific fault, this technique is broad enough to be applied to other types of faults and settings.

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Section: Implementation and Resultsmentioning

confidence: 99%

“…We also compared our scheme with the one currently in use by ALMA, and detailed in [8]. Figure 12 shows a histogram with the days gained by the ESN-based scheme.…”

Section: Implementation and Resultsmentioning

confidence: 99%

Section: Implementation and Resultsmentioning

confidence: 99%

Section: A Literature Reviewmentioning

confidence: 99%

Section: A Problem Descriptionmentioning

confidence: 99%

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Slow Degradation Fault Detection in a Harsh Environment

et al. 2020

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Tool wear prediction using long short-term memory variants and hybrid feature selection techniques

Sayyad

Kumar

Bongale

et al. 2022

Int J Adv Manuf Technol

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A machine learning-based approach for product maintenance prediction with reliability information conversion

Zhang

Xue

Yan

et al. 2022

Auton. Intell. Syst.

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Predictive maintenance (PdM) cannot only avoid economic losses caused by improper maintenance but also maximize the operation reliability of product. It has become the core of operation management. As an important issue in PdM, the time between failures (TBF) prediction can realize early detection and maintenance of products. The reliability information is the main basis for TBF prediction. Therefore, the main purpose of this paper is to establish an intelligent TBF prediction model for complex mechanical products. The reliability information conversion method is used to solve the problems of reliability information collection difficulty, high collection cost and small data samples in the process of TBF prediction based on reliability information for complex mechanical products. The product reliability information is fully mined and enriched to obtain more reliable and accurate TBF prediction results. Firstly, the Fisher algorithm is employed to convert the reliability information to expand the sample, and the compatibility test is also discussed. Secondly, BP neural network is used to realize the final prediction of TBF, and PSO algorithm is used to optimize the initial weight and threshold of BP neural network to avoid falling into local extreme value and improve the convergence speed. Thirdly, the mean-absolute-percentage-error and the Coefficient of determination are selected to evaluate the performance of the proposed model and method. Finally, a case study of TBF prediction for a remanufactured CNC milling machine tool (XK6032-01) is studied in this paper, and the results show that the feasibility and superiority of the proposed TBF prediction method.

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Model-based fault detection and diagnosis in ALMA subsystems

Cited by 10 publications

References 21 publications

Slow Degradation Fault Detection in a Harsh Environment

Slow Degradation Fault Detection in a Harsh Environment

Tool wear prediction using long short-term memory variants and hybrid feature selection techniques

A machine learning-based approach for product maintenance prediction with reliability information conversion

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