The mining industry faces increasing challenges in maintaining high production levels while minimizing unplanned failures and operational costs. Critical assets, such as crushers, conveyor belts, mills, and ventilatio...
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The mining industry faces increasing challenges in maintaining high production levels while minimizing unplanned failures and operational costs. Critical assets, such as crushers, conveyor belts, mills, and ventilation systems, operate under extreme conditions, leading to accelerated wear and failure risks. Traditional maintenance strategies often fail to prevent unexpected downtimes, safety hazards, and economic losses. As a response, industries are integrating predictive monitoring technologies, including machine learning, the Internet of Things, and digital twins, to enhance early faultdetection and optimize maintenance strategies. This Systematic Literature Review analyzes 166 high-impact studies from Scopus and Web of Science, identifying key trends in fault detection algorithms, hybrid AI models, and real-time monitoring techniques. The findings highlight the increasing adoption of deep learning, reinforcement learning, and digital twins for anomaly detection and process optimization. Additionally, AI-driven methods are improving sensor-based data acquisition and asset management, extending equipment lifecycles while reducing failures. Despite these advancements, challenges such as data standardization, model scalability, and system interoperability persist, requiring further research. Future work should focus on real-time AI applications, explainable models, and academia-industry collaboration to accelerate the implementation of intelligent maintenance solutions, ensuring greater reliability, efficiency, and sustainability in mining operations.
Protection performance is characterized by qualities that include selectivity, speed, sensitivity and stability. Generally, selectivity and speed present conflicting requirements on the protection design, as do sensit...
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Protection performance is characterized by qualities that include selectivity, speed, sensitivity and stability. Generally, selectivity and speed present conflicting requirements on the protection design, as do sensitivity and stability requirements. The integration of distributed generation (DG), in particular at the distribution level, further compromises satisfactory achievement of these performance qualities. A recent paper by the authors introduced a protection algorithm based on active power differential and sensitivity analysis (APdSA) for the protection of active distribution systems and microgrids. This paper investigates the performance characteristics of this algorithm with respect to selectivity, sensitivity, stability, and speed. It is shown that, with an important modification to the protection zoning arrangement, the APdSA algorithm is able to selectively clear faults in a DG-integrated distribution system with high sensitivity, stability, and speed, with more than 95% reach over a wide range of fault resistances. The algorithm is also tolerant to communication failure.
A hidden Markov model method proposed earlier for passive acoustic leak detection in sodium fast reactor systems has been improved in order to clarify how to set all free model parameters and to allow smaller amounts ...
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A hidden Markov model method proposed earlier for passive acoustic leak detection in sodium fast reactor systems has been improved in order to clarify how to set all free model parameters and to allow smaller amounts of training data. The method is based on training the model on known background noise only and optimizing its free model parameters by a parametric study of detection performance for synthetic noises superposed onto the same background. This means that the method is not assuming any knowledge on the noise to be detected and may be used as a general faultdetection method, even if the application envisaged here is leak detection for sodium fast reactors. Using recordings of background noise as Well as from argon injection tests performed at full power in the Phenix sodium fast reactor plant, it is estimated that the resulting method will detect leak-like deviations from the background noise with a detection delay of a few seconds, a false alarm rate close to 10(-8) per second and at signal-to-noise ratio conditions at least corresponding to an additive signal at -10 dB. The method is one-channel, i.e. using input from one single acoustic sensor only. (C) 2016 Elsevier Ltd. All rights reserved.
The growing dependence on electronic sensors has resulted in increased concerns over the fault tolerance in safety-critical systems. For example, a vehicle with an advanced driver assistance system, which assists safe...
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The growing dependence on electronic sensors has resulted in increased concerns over the fault tolerance in safety-critical systems. For example, a vehicle with an advanced driver assistance system, which assists safe driving using numerous electronic elements, should be fault tolerant because sensor failures can lead to events in which car occupants receive serious injuries. However, it is often undesirable to have multiple redundant sensors because of the high cost of critical sensors such as the radar in an advanced driver assistance system. To address this problem, we present an analytical hybrid redundancy system that provides fault tolerance using faultdetection and exception-handling algorithms. On the basis of the actual range data from an advanced driver assistance system radar, we present the results of numerical simulations of the analytical hybrid redundancy system and show that it outperforms existing approaches with respect to a number of performance indices.
This paper targets the identification of faulty sensors in sensor networks with faulty sensors. We propose a novel algorithm for faulty sensor identification. Our algorithm is purely localized and thus is suitable for...
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ISBN:
(纸本)0889865639
This paper targets the identification of faulty sensors in sensor networks with faulty sensors. We propose a novel algorithm for faulty sensor identification. Our algorithm is purely localized and thus is suitable for large scale of sensor networks. The computational overhead is low since the detection algorithm, which is a clustering algorithm, is quite simple. Simulation results show that our algorithm can identify faulty sensors with a very high accuracy and a low false alarm rate when as many as 25% sensors become faulty. Our algorithm, therefore, achieves a great improvement over the previous algorithms.
The paper describes a Heuristic Misfire detection strategy, called MEDOC, based on flywheel speed analysis. Tests carried out by means of numerical simulation show both control algorithm robustness and “easy tuning”...
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The paper describes a Heuristic Misfire detection strategy, called MEDOC, based on flywheel speed analysis. Tests carried out by means of numerical simulation show both control algorithm robustness and “easy tuning”. MEDOC low CPU load request, low memory occupation and good results in the actual case study prove that its application in automotive industry can be cost effective and market competitive.
This correspondence discusses three analytical models for intermittent faults in digital systems. These models attempt to represent the stochastic behavior of intermittent faults accurately. The models find applicatio...
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