On Fault Diagnosis for High-G Accelerometers via Data-Driven Models

Abstract

Shock test is a pivotal stage for designing and manufacturing space instruments. As the essential components in shock test systems to measure shock signals accurately, high-g accelerometers are usually exposed to hazardous shock environment and could be subjected to various damages. Owing to that these damages to the accelerometers could result in erroneous measurements which would further lead to shock test failures, accurately diagnosing the fault type of each high-g accelerometer can be vital to ensure the reliability of the shock test experiments. Additionally, in practice, an accelerometer in one malfunction form usually outputs mutable signal waveforms, so that it is difficult to empirically judge the fault type of the accelerometer based on the erroneous readings. Moreover, traditional hardware diagnosis approaches require disassembling the sensor's package shell and manually observing the damage of the elements inner the sensor, which are less efficient and uneconomical. Aiming at these problems, several data-driven approaches are incorporated to diagnose the fault types of high-g accelerometers in this work. Firstly, several high-g accelerometers with most frequent types of damage are collected, and a shock signal dataset is gathered by conducting shock tests on these faulty accelerometers. Then, the obtained dataset is used to train several base classifiers to identify the fault types in a supervised fashion. Lastly, a hybrid ensemble learning model is established by integrating these base classifiers with both heterogeneous and homogeneous models. Experimental results show that these data-driven methods can accurately identify the fault types of high-g accelerometers from their mutable erroneous readings. Index Terms-shock test, high-g accelerometer, fault diagnosis, data-driven methods, ensemble learning.

Self-validating high-g accelerometers through data-driven methods

“…Subsequently, charges will be generated on the upper and lower surfaces of the piezoelectric element. Lastly, the acceleration of the testing object can be measured from the charges after amplification [27].…”

“…Sensibly, the complexity of the kNN model is mainly dominated by the hyper-parameter k. Smaller k value corresponds to more complex model as the prediction from kNN could be more easily influenced by movements in the feature space. kNN is simple to be implemented, but our previous work [27] shows that standalone kNN classifier cannot achieve the best classification accuracy for faulty high-g accelerometers. A feasible strategy for improving the classification accuracy is integrating multiple individual kNN classifiers with ensemble learning [27,46].…”

“…In our previous work, two deep neural networks (DNNs) were developed to recover shock signals of two forms of fault respectively [8,9]. In addition, we also proposed an ensemble learning-based model to identify the fault types of high-g accelerometers by integrating several data-driven-based sub-classifiers [27]. However, these works cannot form an integral self-validation strategy to self-validate high-g accelerometers in pyroshock tests.…”

“…This hybrid method integrates the spline interpolation algorithm and Savitzky-Golay filtering and provides improvements in multiple metrics with lower calculation costs. Inspired by these works, a hybrid strategy is proposed in this work to self-validate high-g accelerometers by integrating the ensemble kNNs and the DNN-based recovery method in our previous works [8,9,27].…”

Sensor Fault Diagnosis Method Based on α‐Grey Wolf Optimization‐Support Vector Machine

“…Abnormal data are considered the most effective indicators of sensor failure, which are nonlinear and enormous and make the data-driven intelligent diagnosis method more suitable for the diagnosis of sensor fault [11][12][13]. Machine learning algorithm is a commonly used method for intelligent diagnosis, including Neural Networks (NNs), Support Vector Machines (SVMs), and so on.…”