Electromagnetic Relays (Electromagnetic Relay (EMR)s) are omnipresent in electrical systems, ranging from mass-produced consumer products to highly specialised, safety-critical industrial systems. Our detailed literature review focused on EMR reliability highlighting the methods used to estimate the State of Health or the Remaining Useful Life emphasises the limited analysis and understanding of expressive EMR degradation indicators, as well as accessibility and use of EMR life cycle data sets. Prioritising these open challenges, a deep learning pipeline is presented in a prognostic context termed Electromagnetic Relay Useful Actuation Pipeline (EMRUA). Leveraging the attributes of causal convolution, a Temporal Convolutional Network (TCN) based architecture integrates an arbitrary long sequence of multiple features to produce a remaining useful switching actuations forecast. These features are extracted from raw, high volume life cycle data sets, namely EMR switching data (Contact-Voltage, Contact-Current). Monte-Carlo Dropout is utilised to estimate uncertainty during inference. The TCN hyperparameter space, as well as various methods to select and analyse long sequences of multivariate time series data are investigated. Subsequently, our results demonstrate improvements using the developed statistical feature-set over traditional, time-based features, commonly found in literature. EMRUA achieves an average forecasting mean absolute percentage error of ±12 % over the course of the entire EMR life.@en

In this paper, failure analysis of an electromagnetic relay is studied to inform embedded health monitoring for the prediction of Remaining Useful Life (RUL) of electromagnetic relays. A statistical design of experiment with online monitoring of voltage, current, temperature, resistance, duty cycle, and frequency is used to identify relay failure precursors. Examples of detected precursors include abnormal spikes in resistance and extension of the average closing time of the relay within the final 1.5 million actuations prior to failure. A detailed post-failure analysis utilising white light interferometry and energy dispersive X-ray (EDX) analysis reveals the relay failure modes associated with erosion, pit and crater formation, and material transfer. The results demonstrate that low resolution data can detect signatures of impending failure modes such as stuck open and closed failures as well as unacceptable long opening times. The findings within this paper demonstrate remote monitoring of the near real-time health of electromagnetic relays with low resolution data analysis.@en

Industrial-scale component maintenance is shifting towards novel Predictive Maintenance (PdM) strategies supported by Big Data Analytics (BDA). This has resulted in an increased effort to implement Artificial Intelligence (AI) decision making into new maintenance paradigms. The transition of AI into industry faces significant challenges due to the inherent complexities of industrial operations, such as variability in components due to manufacturing, integration, dynamic operating environments and variable loading conditions. Therefore, AI in critical industrial systems requires more advanced capabilities such as robustness, scalability and verifiability. This paper presents the first Deep Learning (DL) based strategy for the classification of the State-Of-Health (SOH) of Electromagnetic Relays (EMR). The DL strategy scales with high-volumes of multivariate time-series data whilst automating labour intensive feature extraction requirements. The method proposed in our paper, combines a Convolutional-Auto-Encoder (CAE) with a Temporal Convolutional Neural Network (TCN), referred to as EMR-SOH CAE-TCN pipeline. Model uncertainty and SOH confidence bounds are approximated by Monte-Carlo dropout. Our pipeline is trained and evaluated on data generated from EMR life-cycle tests. We report a high classification accuracy and discriminatory power of the EMR-SOH classifier. The findings from our paper demonstrate the potential of AI pipelines for maintenance decision making of components in critical applications, providing a transferable AI based PdM solution that scales with large data quantities.@en

Downhole tools are complex electro-mechanical systems that perform critical functions in drilling operations. The electronics within these systems provide vital support, such as control, navigation and front-end data analysis from sensors. Due to the extremely challenging operating conditions, namely high pressure, temperature and vibrational forces, electronics can be subjected to complex failure modes and incur operational downtime. A novel Artificial Intelligence (AI)-driven Condition Based Maintenance (CBM) support system is presented, combining Bottom Hole Assembly (BHA) data with Big Data Analytics (BDA). The key objective of this system is to reduce maintenance costs along with an overall improvement of fleet reliability. As evidenced within the literature review, the application of AI methods to downhole tool maintenance is underrepresented in terms of oil and gas application. We review the BHA electronics failure modes and propose a methodology for BHA-Printed Component Board Assemblies (PCBA) CBM. We compare the results of a Random Forest Classifier (RFC) and a XGBoost Classifier trained on BHA electronics memory data cumulated during 208 missions over a 6 months period, achieving an accuracy of 90 % for predicting PCBA failure. These results are extended into a commercial analysis examining various scenarios of infield failure costs and fleet reliability levels. The findings of this paper demonstrate the value of the BHA-PCBA CBM framework by providing accurate prognosis of operational equipment health leading to reduced costs, minimised Non-Productive Time (NPT) and increased operational reliability.@en

Downhole tools are complex electro-mechanical systems that perform critical functions in drilling operations. The electronics within these systems provide vital support, such as control, navigation and front-end data analysis from sensors. Due to the extremely challenging operating conditions, namely high pressure, temperature and vibrational forces, electronics can be subjected to complex failure modes and incur operational downtime. A novel Artificial Intelligence (AI)-driven Condition Based Maintenance (CBM) support system is presented, combining Bottom Hole Assembly (BHA) data with Big Data Analytics (BDA). The key objective of this system is to reduce maintenance costs along with an overall improvement of fleet reliability. As evidenced within the literature review, the application of AI methods to downhole tool maintenance is underrepresented in terms of oil and gas application. We review the BHA electronics failure modes and propose a methodology for BHA-Printed Component Board Assemblies (PCBA) CBM. We compare the results of a Random Forest Classifier (RFC) and a XGBoost Classifier trained on BHA electronics memory data cumulated during 208 missions over a 6 months period, achieving an accuracy of 90 % for predicting PCBA failure. These results are extended into a commercial analysis examining various scenarios of infield failure costs and fleet reliability levels. The findings of this paper demonstrate the value of the BHA-PCBA CBM framework by providing accurate prognosis of operational equipment health leading to reduced costs, minimised Non-Productive Time (NPT) and increased operational reliability.@en