Quantitative evaluation of electric features for health monitoring and assessment of AC-powered solenoid operated valves

Quantitative assessment of feature performance for health monitoring is key to feature selection. This paper illustrates the application of well-established metrics in the research community - namely, monotonicity, robustness and prognosability - to the quantitative performance assessment of features for health monitoring of alternating-current (AC) powered solenoid operated valves (SOVs). These features are extracted from voltage and current signals measured on the valves and builds on previous work of the authors. Based on these metrics, the appropriate features are selected to be used as condition indicators. The selected features are inputs to a logistic regression model to predict a health index ranging from 0 to 1, which can be easily monitored and assessed by non-experts. We demonstrated the developed methodology on the experimental data acquired from accelerated life tests on 48 identical AC-powered SOVs.This research was supported by both Flanders Make, the strategic research center for the manufacturing industry, and VLAIO, Flanders Innovation and Entrepreneurship, within the framework of the MODA-ICON project

An improved first-principle model of AC powered solenoid operated valves for maintenance applications

Solenoid operated valves (SOVs) are critical components in many industrial applications. There has been a continuing interest in the industry to have robust condition monitoring, prognostics and health management tools to support the condition based maintenance and predictive maintenance program for such valves. For critical assets like SOVs, it is of paramount interest to understand why a component might be declared as defective. In such a situation, a first principle-model based approach will always be preferred to a purely data-driven approach, because of its inherent interpretability. Furthermore, first principle-models typically have less free parameters than their data driven counterparts and will require less data to identify their parameters. In this paper, we present the improvement of a first-principle model of alternating current (AC) powered SOVs taking into account two important degradation effects. Using this model, we show that the state of degradation can be estimated from current and input voltage measurement signals on the solenoids. Our method is validated using data from an accelerated life test campaign on 48 identical AC-powered SOVs

Explainable data-driven method combined with Bayesian filtering for remaining useful lifetime prediction of aircraft engines using NASA CMAPSS datasets

An aircraft engine is expected to have a high-reliability system as a safety-critical asset. A scheduled maintenance strategy based on statistical calculation has been employed as the current practice to achieve the reliability requirement. Any improvement to this maintenance interval is made after significant reliability issues arise (such as flight delays and high component removals). Several publications and research studies have been conducted related to this issue, one of them involves performing simulations and providing aircraft operation datasets. The recently published NASA CMAPPS datasets have been utilised in this paper since they simulate flight data recording from various measurements. A prognostics model can be developed by analysing these datasets and predicting the engine’s reliability before failure. However, the state-of-the-art prognostics techniques published in the literature using these NASA CMAPPS datasets are mainly purely data-driven. These techniques mainly deal with a “black box” process which does not include uncertainty quantification (UQ). These two factors are barriers to prognostics applications, particularly in the aviation industry. To tackle these issues, this paper aims at developing explainable and transparent algorithms and a software tool to compute the engine health, estimate engine end of life (EoL), and eventually predict its remaining useful life (RUL). The proposed algorithms use hybrid metrics for feature selection, employ logistic regression for health index estimation, and unscented Kalman filter (UKF) to update the prognostics model for predicting the RUL in a recursive fashion. Among the available datasets, dataset 02 is chosen because it has been widely used and is an ideal candidate for result comparison and dataset 03 is employed as a new state-of-the-art. As a result, the proposed algorithms yield 34.5–55.6% better performance in terms of the root mean squared error (RMSE) compared with the previous work. More importantly, the proposed method is transparent and it quantifies the uncertainty during the prediction process

Switching Kalman filtering-based corrosion detection and prognostics for offshore wind-turbine structures

Since manual inspections of offshore wind turbines are costly, there is a need for remote monitoring of their health condition, including health prognostics. In this paper, we focus on corrosion detection and corrosion prognosis since corrosion is a major failure mode of offshore wind turbine structures. In particular, we propose an algorithm for corrosion detection and three algorithms for corrosion prognosis by using Bayesian filtering approaches, and quantitatively compare their accuracy against synthetic datasets having characteristics typical for wall thickness measurements using ultrasound sensors. We found that a corrosion prognosis algorithm based on the Pourbaix corrosion model using unscented Kalman filtering outperforms the algorithms based on a linear corrosion model and the bimodal corrosion model introduced by Melchers

A convolutional neural network aided physical model improvement for AC solenoid valves diagnosis

This paper focuses on the development of a physics-based diagnostic tool for alternating current (AC) solenoid valves which are categorized as critical components of many machines used in the process industry. Signal processing and machine learning based approaches have been proposed in the literature to diagnose the health state of solenoid valves. However, the approaches do not give a physical explanation of the failure modes. In this work, being capable of diagnosing failure modes while using a physically interpretable model is proposed. Feature attribution methods are applied to CNN on a large data set of the current signals acquired from accelerated life tests of several AC solenoid valves. The results reveal important regions of interest on current signals that guide the modeling of the main missing component of an existing physical model. Two model parameters, which are the shading ring and kinetic coulomb forces, are then identified using current measurements along the lifetime of valves. Consistent trends are found for both parameters allowing to diagnose the failure modes of the solenoid valves. Future work will consist of not only diagnosing the failure modes, but also of predicting the remaining useful life

Detection, prognosis and decision support tool for offshore wind turbine structures

Corrosion is the leading cause of failure for Offshore Wind Turbine (OWT) structures and it is characterized by a low probability of detection. With focus on uniform corrosion, we propose a corrosion detection and prognosis system coupled with a Decision Support Tool (DST) and a Graphical User Interface (GUI). By considering wall thickness measurements at different critical points along the wind turbine tower, the proposed corrosion detection and prognosis system—based on Kalman filtering, empirical corrosion models and reliability theory—estimates the Remaining Useful Life of the structure with regard to uniform corrosion. The DST provides a systematic approach for evaluating the results of the prognosis module together with economical information, to assess the different possible actions and their optimal timing. Focus is placed on the optimization of the decommissioning time of OWTs. The case of decommissioning is relevant as corrosion—especially in the splash zone of the tower—makes maintenance difficult and very costly, and corrosion inevitably leads to the end of life of the OWT structure. The proposed algorithms are illustrated with examples. The custom GUI facilitates the interpretation of results of the prognosis module and the economical optimization, and the interaction with the user for setting the different parameters and costs involved.European Union funding: 85120

Review of corrosion monitoring and prognostics in offshore wind turbine structures: current status and feasible approaches

As large wind farms are now often operating far from the shore, remote condition monitoring and condition prognostics become necessary to avoid excessive operation and maintenance costs while ensuring reliable operation. Corrosion, and in particular uniform corrosion, is a leading cause of failure for Offshore Wind Turbine (OWT) structures due to the harsh and highly corrosive environmental conditions in which they operate. This paper reviews the state-of-the-art in corrosion mechanism and models, corrosion monitoring and corrosion prognostics with a view on the applicability to OWT structures. Moreover, we discuss research challenges and open issues as well strategic directions for future research and development of cost-effective solutions for corrosion monitoring and prognostics for OWT structures. In particular, we point out the suitability of non-destructive autonomous corrosion monitoring systems based on ultrasound measurements, combined with hybrid prognosis methods based on Bayesian Filtering and corrosion empirical models

Low-cost vibration sensor with low frequency resonance for condition monitoring of low speed bearings: a feasibility study

Condition monitoring (CM) of rolling element bearings (REBs) rotating at low speeds poses some challenges in practice because of the low signal-to-noise ratio produced by the faults. Both vibration-based and ultrasound/acoustic emission (AE) based sensing techniques have been proposed in the literature to detect faults in rolling element bearings running at low speeds. The vibration-based technique generally works within the frequency band from 0 to 20 kHz. Meanwhile, the ultrasound/AE-based techniques work in a very high-frequency band, from 20 kHz up to 1 MHz. Consequently, processing ultrasound/AE sensor data requires more computational resources compared to processing vibration data. Moreover, the hardware investment to build an ultrasound/AE-based CM system is more expensive than that of a vibration-based CM system. Since hardware and software cost is one of the main bottlenecks of the adoption of CM systems in the industry, it is, therefore, necessary to develop a cost-effective vibration-based CM system for critical bearings mounted on low-rotational speed machines. The paper presents a feasibility study in evaluating the performance of an off-the-shelf low-cost vibration sensor (10 – 20 times cheaper than high-end vibration sensors) with a low resonance frequency (around 75 Hz) to diagnose faults on REBs operating at a low rotational speed of 30 - 60 rpm. The low-resonance frequency characteristic of the low-cost sensor allows us to acquire the data at a low sampling rate of 400 Hz. A theoretical justification of why a vibration sensor with low-resonance frequency can still be effective for low-speed bearing fault diagnosis is given. This feasibility was experimentally validated on a test rig on which an REB with a seeded fault on the outer race was tested as a case study. A high-end vibration sensor (accelerometer) acquired at a 20 kHz sampling rate was also used as a benchmark. The vibration signals measured by the low-cost and high-end sensors in four different operating conditions were analysed with the well-established envelope analysis. In addition, the high-end sensor signals were also analysed with the state-of-the-art Spectral Correlation (SC) technique to compute the Enhanced Envelope Spectrum (EES) for bearing fault detection and diagnosis. The results confirmed that the bearing fault could be successfully detected and diagnosed in all the test conditions by the low-cost sensor analysed with the envelope analysis technique. On the other hand, the high-end sensor analysed with the SC technique could only diagnose the bearing fault for the least challenging test condition. The outstanding diagnostic capability of the low-cost sensor sampled at a low sampling rate has set a milestone that would enable the future development of a low-cost CM system for low-speed bearing applications

Explainable Data-Driven Method Combined with Bayesian Filtering for Remaining Useful Lifetime Prediction of Aircraft Engines Using NASA CMAPSS Datasets

An aircraft engine is expected to have a high-reliability system as a safety-critical asset. A scheduled maintenance strategy based on statistical calculation has been employed as the current practice to achieve the reliability requirement. Any improvement to this maintenance interval is made after significant reliability issues arise (such as flight delays and high component removals). Several publications and research studies have been conducted related to this issue, one of them involves performing simulations and providing aircraft operation datasets. The recently published NASA CMAPPS datasets have been utilised in this paper since they simulate flight data recording from various measurements. A prognostics model can be developed by analysing these datasets and predicting the engine’s reliability before failure. However, the state-of-the-art prognostics techniques published in the literature using these NASA CMAPPS datasets are mainly purely data-driven. These techniques mainly deal with a “black box” process which does not include uncertainty quantification (UQ). These two factors are barriers to prognostics applications, particularly in the aviation industry. To tackle these issues, this paper aims at developing explainable and transparent algorithms and a software tool to compute the engine health, estimate engine end of life (EoL), and eventually predict its remaining useful life (RUL). The proposed algorithms use hybrid metrics for feature selection, employ logistic regression for health index estimation, and unscented Kalman filter (UKF) to update the prognostics model for predicting the RUL in a recursive fashion. Among the available datasets, dataset 02 is chosen because it has been widely used and is an ideal candidate for result comparison and dataset 03 is employed as a new state-of-the-art. As a result, the proposed algorithms yield 34.5–55.6% better performance in terms of the root mean squared error (RMSE) compared with the previous work. More importantly, the proposed method is transparent and it quantifies the uncertainty during the prediction process