Slow Degradation Fault Detection in a Harsh Environment

Abstract

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.

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

“…This work focuses on prognostics by developing recurrent neural networks (RNNs) and a forecasting method called Prophet to measure the performance quality in RUL estimation. First, we apply this approach to degradation signals, which do not need to be monotonical, using the fault detection framework proposed in [ 15 ] with some improvements in the pre-processing and the cleaning data step. Later, we applied our approach to similar degradation problems but with different statistical characteristics.…”

“…Our work has the following contributions: We made improvements in cleaning spikes or possible outlines and smoothing time-series in the pre-processing data step in the fault detection framework developed in [ 15 ] to reduce the remaining noise level while maintaining its relevant characteristics such as trends and stationarity. We show that the fault detection framework in [ 15 ], together with our pre-processing method, improves the robustness of the framework and can be transferable to another problem with similar degradation, although with different statistical characteristics. We built a strategy using clustering run-to-failure critical segments to define an appropriate failure threshold that improves the RUL estimation.…”

“…We made improvements in cleaning spikes or possible outlines and smoothing time-series in the pre-processing data step in the fault detection framework developed in [ 15 ] to reduce the remaining noise level while maintaining its relevant characteristics such as trends and stationarity.…”

“…We show that the fault detection framework in [ 15 ], together with our pre-processing method, improves the robustness of the framework and can be transferable to another problem with similar degradation, although with different statistical characteristics.…”

“…Fault detection systems were developed around the 1970s [ 4 , 16 ], as an essential part of automatic control systems to maintain desirable performance. Fault detection can be defined as a process of determining if a system or subsystem has entered a mode different from the normal operating condition [ 15 ], and a fault may appear at an unknown time, and the speed of appearance may be different [ 17 , 18 ].…”