ALMA engineering fault detection framework

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

doi:10.1117/12.2312285

Cited by 3 publications

(5 citation statements)

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“…This method is implemented in the ADTK library [ 59 ]. Double exponential smoothing : this filter [ 26 , 60 , 61 , 62 , 63 , 64 ] is commonly used for forecasting in time series, but it can also be used for noise reduction. This method is particularly useful in time series to smooth its behavior, preserving the trend and without losing almost any information in the dynamics of the series.…”

Section: Methodsmentioning

confidence: 99%

“…From an application perspective, fault detection systems have been developed in many areas such as rolling bearing, machines, industrial systems, mechatronics systems, industrial cyber-physical systems, and industrial-scale telescopes, to name a few [15,[23][24][25][26][33][34][35]37,38,41,42]. Some of them describe some advantages and disadvantages over others in the applied methodology to obtain better results.…”

Section: Fault Detectionmentioning

confidence: 99%

“…In the literature, a wide variety of methods used for fault detection can be classified into signal processing approaches [ 18 , 19 , 20 , 21 , 22 , 23 ], model-based approaches [ 23 , 24 , 25 , 26 ], knowledge-based approaches [ 18 , 27 , 28 , 29 ], and data-driven approaches [ 18 , 23 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. With the arrival of technology and the advancement of computing methods, data-driven approaches are gaining attention in the last decades, where it is expected that the data will drive the identification of normal and faulty modes of operation.…”

Section: Introductionmentioning

confidence: 99%

“…From an application perspective, fault detection systems have been developed in many areas such as rolling bearing, machines, industrial systems, mechatronics systems, industrial cyber-physical systems, and industrial-scale telescopes, to name a few [ 15 , 23 , 24 , 25 , 26 , 33 , 34 , 35 , 37 , 38 , 41 , 42 ].…”

Section: Introductionmentioning

confidence: 99%

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A RUL Estimation System from Clustered Run-to-Failure Degradation Signals

Cho

Carrasco

Ruz

2022

Sensors

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The prognostics and health management disciplines provide an efficient solution to improve a system’s durability, taking advantage of its lifespan in functionality before a failure appears. Prognostics are performed to estimate the system or subsystem’s remaining useful life (RUL). This estimation can be used as a supply in decision-making within maintenance plans and procedures. This work focuses on prognostics by developing a recurrent neural network and a forecasting method called Prophet to measure the performance quality in RUL estimation. We apply this approach to degradation signals, which do not need to be monotonical. Finally, we test our system using data from new generation telescopes in real-world applications.

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Section: Methodsmentioning

confidence: 99%

Section: Fault Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…From an application perspective, fault detection systems have been developed in many areas such as rolling bearing, machines, industrial systems, mechatronics systems, industrial cyber-physical systems, and industrial-scale telescopes, to name a few [ 15 , 23 , 24 , 25 , 26 , 33 , 34 , 35 , 37 , 38 , 41 , 42 ].…”

Section: Introductionmentioning

confidence: 99%

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A RUL Estimation System from Clustered Run-to-Failure Degradation Signals

Cho

Carrasco

Ruz

2022

Sensors

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“…There are few works related to natural disasters applied to industrial-scale telescopes, mostly focused on astronomical studies to analyze the phenomena and celestial bodies (Ray et al, 2022;Yushchenko et al, 2022), construction or partial or total improvement of the system (Marchiori et al, 2018;Morzinski et al, 2014;Allekotte et al, 2013), fault detection (Ortiz and Carrasco, 2018;Cho et al, 2020;Wu et al, 2020;Roelf, 2022), prognosis Roelf, 2022), and decision-making in maintenance (Costa et al, 2021;, among others.…”

Section: Previous Workmentioning

confidence: 99%

Reliability of industrial-scale telescopes in the event of seismic failures

Cho,

Jiménez,

Guevara

2024

Preprint

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Natural disasters have the potential to pose a significant threat to property, critical infrastructure, human health and safety, and some others. One of the most relevant natural disasters is earthquakes, which are high on the list of natural phenomena that most affect infrastructures and, at the same time, are the most unpredictable. This study uses the probabilistic seismic risk analysis method to estimate the condition of an industrial-scale telescope after the effect of an earthquake. The approach considers a seismic source model and ground motion prediction equations to evaluate the intensity measure of each telescope as a function of its location. Our simulation uses reliability via Monte Carlo to discover the probability of failure of each telescope taking into consideration the fragility curves described by its nature structure. Our method incorporates terrain characteristics and component robustness into the analysis of telescope performance at an affordable computational cost. We applied our method to the observatories that use industrial-scale telescopes established in Chile, and we showed that reliability is highly dependent on the robustness/fragility of the infrastructure. MSC Classification: 62P12 , 62P30 , 90B25

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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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ALMA engineering fault detection framework

Cited by 3 publications

References 1 publication

A RUL Estimation System from Clustered Run-to-Failure Degradation Signals

A RUL Estimation System from Clustered Run-to-Failure Degradation Signals

Reliability of industrial-scale telescopes in the event of seismic failures

Slow Degradation Fault Detection in a Harsh Environment

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