arXiv subjects

Benoit Boulet

Publications and source records attributed to Benoit Boulet.

31 records · Page 2Linked to original sources

arXiv2023

Meta-Learning Based Early Fault Detection for Rolling Bearings via Few-Shot Anomaly Detection

Early fault detection (EFD) of rolling bearings can recognize slight deviation of the health states and contribute to the stability of mechanical systems. In practice, very limited target bearing data are available to conduct EFD, which makes it hard to adapt to the EFD task of new bearings. To address this problem, many transfer learning based EFD methods utilize historical data to learn transferable domain knowledge and conduct early fault detection on new target bearings. However, most existing methods only consider the distribution drift across different working conditions but ignore the difference between bearings under the same working condition, which is called Unit-to-Unit Variability (UtUV). The setting of EFD with limited target data considering UtUV can be formulated as a Few-shot Anomaly Detection task. Therefore, this paper proposes a novel EFD method based on meta-learning considering UtUV. The proposed method can learn a generic metric based on Relation Network (RN) to measure the similarity between normal data and the new arrival target bearing data. Besides, the proposed method utilizes a health state embedding strategy to decrease false alarms. The performance of proposed method is tested on two bearing datasets. The results show that the proposed method can detect incipient faults earlier than the baselines with lower false alarms.

cs.LG↗

arXiv2023

An Early Fault Detection Method of Rotating Machines Based on Multiple Feature Fusion with Stacking Architecture

Early fault detection (EFD) of rotating machines is important to decrease the maintenance cost and improve the mechanical system stability. One of the key points of EFD is developing a generic model to extract robust and discriminative features from different equipment for early fault detection. Most existing EFD methods focus on learning fault representation by one type of feature. However, a combination of multiple features can capture a more comprehensive representation of system state. In this paper, we propose an EFD method based on multiple feature fusion with stacking architecture (M2FSA). The proposed method can extract generic and discriminiative features to detect early faults by combining time domain (TD), frequency domain (FD), and time-frequency domain (TFD) features. In order to unify the dimensions of the different domain features, Stacked Denoising Autoencoder (SDAE) is utilized to learn deep features in three domains. The architecture of the proposed M2FSA consists of two layers. The first layer contains three base models, whose corresponding inputs are different deep features. The outputs of the first layer are concatenated to generate the input to the second layer, which consists of a meta model. The proposed method is tested on three bearing datasets. The results demonstrate that the proposed method is better than existing methods both in sensibility and reliability.

cs.LG↗

arXiv2023

Adaptive Aggregation for Safety-Critical Control

Safety has been recognized as the central obstacle to preventing the use of reinforcement learning (RL) for real-world applications. Different methods have been developed to deal with safety concerns in RL. However, learning reliable RL-based solutions usually require a large number of interactions with the environment. Likewise, how to improve the learning efficiency, specifically, how to utilize transfer learning for safe reinforcement learning, has not been well studied. In this work, we propose an adaptive aggregation framework for safety-critical control. Our method comprises two key techniques: 1) we learn to transfer the safety knowledge by aggregating the multiple source tasks and a target task through the attention network; 2) we separate the goal of improving task performance and reducing constraint violations by utilizing a safeguard. Experiment results demonstrate that our algorithm can achieve fewer safety violations while showing better data efficiency compared with several baselines.

cs.LG↗

arXiv2022

Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection

Time series anomaly detection has been recognized as of critical importance for the reliable and efficient operation of real-world systems. Many anomaly detection methods have been developed based on various assumptions on anomaly characteristics. However, due to the complex nature of real-world data, different anomalies within a time series usually have diverse profiles supporting different anomaly assumptions. This makes it difficult to find a single anomaly detector that can consistently outperform other models. In this work, to harness the benefits of different base models, we propose a reinforcement learning-based model selection framework. Specifically, we first learn a pool of different anomaly detection models, and then utilize reinforcement learning to dynamically select a candidate model from these base models. Experiments on real-world data have demonstrated that the proposed strategy can indeed outplay all baseline models in terms of overall performance.

cs.LG↗

arXiv2022

Learning-based synthesis of robust linear time-invariant controllers

Recent advances in learning for control allow to synthesize vehicle controllers from learned system dynamics and maintain robust stability guarantees. However, no approach is well-suited for training linear time-invariant (LTI) controllers using arbitrary learned models of the dynamics. This article introduces a method to do so. It uses a robust control framework to derive robust stability criteria. It also uses simulated policy rollouts to obtain gradients on the controller parameters, which serve to improve the closed-loop performance. By formulating the stability criteria as penalties with computable gradients, they can be used to guide the controller parameters toward robust stability during gradient descent. The approach is flexible as it does not restrict the type of learned model for the simulated rollouts. The robust control framework ensures that the controller is already robustly stabilizing when first implemented on the actual system and no data is yet collected. It also ensures that the system stays stable in the event of a shift in dynamics, given the system behavior remains within assumed uncertainty bounds. We demonstrate the approach by synthesizing a controller for simulated autonomous lane change maneuvers. This work thus presents a flexible approach to learning robustly stabilizing LTI controllers that take advantage of modern machine learning techniques.

eess.SY↗

arXiv2022

Structured learning of safety guarantees for the control of uncertain dynamical systems

Approaches to keeping a dynamical system within state constraints typically rely on a model-based safety condition to limit the control signals. In the face of significant modeling uncertainty, the system can suffer from important performance penalties due to the safety condition becoming overly conservative. Machine learning can be employed to reduce the uncertainty around the system dynamics, and allow for higher performance. In this article, we propose the safe uncertainty learning principle, and argue that the learning must be properly structured to preserve safety guarantees. For instance, robust safety conditions are necessary, and they must be initialized with conservative uncertainty bounds prior to learning. Also, the uncertainty bounds should only be tightened if the collected data sufficiently capture the future system behavior. To support the principle, two example problems are solved with control barrier functions: a lane-change controller for an autonomous vehicle, and an adaptive cruise controller. This work offers a way to evaluate if machine learning preserves safety guarantees during the control of uncertain dynamical systems. It also highlights challenging aspects of learning for control.

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arXiv2021

ModelLight: Model-Based Meta-Reinforcement Learning for Traffic Signal Control

Traffic signal control is of critical importance for the effective use of transportation infrastructures. The rapid increase of vehicle traffic and changes in traffic patterns make traffic signal control more and more challenging. Reinforcement Learning (RL)-based algorithms have demonstrated their potential in dealing with traffic signal control. However, most existing solutions require a large amount of training data, which is unacceptable for many real-world scenarios. This paper proposes a novel model-based meta-reinforcement learning framework (ModelLight) for traffic signal control. Within ModelLight, an ensemble of models for road intersections and the optimization-based meta-learning method are used to improve the data efficiency of an RL-based traffic light control method. Experiments on real-world datasets demonstrate that ModelLight can outperform state-of-the-art traffic light control algorithms while substantially reducing the number of required interactions with the real-world environment.

cs.LG↗

arXiv2021

Improving gearshift controllers for electric vehicles with reinforcement learning

During a multi-speed transmission development process, the final calibration of the gearshift controller parameters is usually performed on a physical test bench. Engineers typically treat the mapping from the controller parameters to the gearshift quality as a black-box, and use methods rooted in experimental design -- a purely statistical approach -- to infer the parameter combination that will maximize a chosen gearshift performance indicator. This approach unfortunately requires thousands of gearshift trials, ultimately discouraging the exploration of different control strategies. In this work, we calibrate the feedforward and feedback parameters of a gearshift controller using a model-based reinforcement learning algorithm adapted from Pilco. Experimental results show that the method optimizes the controller parameters with few gearshift trials. This approach can accelerate the exploration of gearshift control strategies, which is especially important for the emerging technology of multi-speed transmissions for electric vehicles.

eess.SY↗

arXiv2021

Time Series Anomaly Detection for Smart Grids: A Survey

With the rapid increase in the integration of renewable energy generation and the wide adoption of various electric appliances, power grids are now faced with more and more challenges. One prominent challenge is to implement efficient anomaly detection for different types of anomalous behaviors within power grids. These anomalous behaviors might be induced by unusual consumption patterns of the users, faulty grid infrastructures, outages, external cyberattacks, or energy fraud. Identifying such anomalies is of critical importance for the reliable and efficient operation of modern power grids. Various methods have been proposed for anomaly detection on power grid time-series data. This paper presents a short survey of the recent advances in anomaly detection for power grid time-series data. Specifically, we first outline current research challenges in the power grid anomaly detection domain and further review the major anomaly detection approaches. Finally, we conclude the survey by identifying the potential directions for future research.

cs.LG↗

arXiv2021

Data-driven Model Predictive and Reinforcement Learning Based Control for Building Energy Management: a Survey

Building energy management is one of the core problems in modern power grids to reduce energy consumption while ensuring occupants' comfort. However, the building energy management system (BEMS) is now facing more challenges and uncertainties with the increasing penetration of renewables and complicated interactions between humans and buildings. Classical model predictive control (MPC) has shown its capacity to reduce building energy consumption, but it suffers from labor-intensive modelling and complex on-line control optimization. Recently, with the growing accessibility to the building control and automation data, data-driven solutions have attracted more research interest. This paper presents a compact review of the recent advances in data-driven MPC and reinforcement learning based control methods for BEMS. The main challenges in these approaches and insights on the selection of a control method are discussed.

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arXiv2021

Fundamental limitations to no-jerk gearshifts of multi-speed transmission architectures in electric vehicles

Multi-speed transmissions can enhance the performance and reduce the overall cost of an electric vehicle, but they also introduce a challenge: avoiding gearshift jerk, which may sometimes prove to be impossible in the presence of motor and clutch saturation. In this article, we introduce three theorems that explicitly define the fundamental limitations to no-jerk gearshifts resulting from motor or actuator saturation. We compare gearshifts that consist of transferring transmission torque from one friction clutch to another, to the case in which one of the clutches is a one-way clutch. We show that systems with a one-way clutch are more prone to motor saturation, causing gearshift jerk to be more often inevitable. We also study the influence of planetary gearsets on the gearshift dynamical trajectories, and expose the impact on the no-jerk limitations. This work offers tools to compare transmission architectures during the conceptual design phase of a new electric vehicle.

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arXiv2021

Image-based Intraluminal Contact Force Monitoring in Robotic Vascular Navigation

Embolization, stroke, ischaemic lesion, and perforation remain significant concerns in endovascular interventions. Intravascular sensing of tool interaction with the arteries is advantageous to minimize such complications and enhance navigation safety. Intraluminal information is currently limited due to the lack of intravascular contact sensing technologies. We present monitoring of the intraluminal tool interaction with the arterial wall using an image-based estimation approach within vascular robotic navigation. The proposed image-based method employs continuous finite element simulation of the tool using imaging data to estimate multi-point forces along tool-vessel wall interaction. We implemented imaging algorithms to detect and track contacts, and compute pose measurements. The model is constructed based on the nonlinear beam element and flexural rigidity profile over the tool length. During remote cannulation of aortic arteries, intraluminal monitoring achieved tracking local contact forces, building a contour map of force on the arterial wall and estimating tool structural stress. Results suggest that high risk intraluminal forces may happen even with low insertion force. The presented online monitoring system delivers insight into the intraluminal behavior of endovascular tools and is well suited for intraoperative visual guidance for the clinician, robotic control of vascular procedures and research on interventional device design.

cs.RO↗

arXiv2019

Centralized Model Predictive Control Strategy for Thermal Comfort and Residential Energy Management

A novel centralized model predictive control (MPC) is proposed for comfort and energy management in a residential building. The residential setup used here is equipped with a photovoltaic (PV) solar system and a stationary home battery unit. An air-to-air multi-split heat pump (HP) is used as the primary heating system. The electric baseboard (BB) unit in each zone is used as a secondary system. The MPC is simultaneously responsible for controlling the heating inputs of the HP and BB units for comfort management, as well as for the control of energy flow between the PV, the home battery and the bidirectional grid system. Variable Time-of-Use (ToU) rates are considered for the energy cost calculation and Feed-in-Tariff (FiT) is considered for selling energy to the grid. A 13.5% reduction in the energy cost is achieved with the centralized MPC as compared to a rule based energy management strategy. The solar energy generation and battery storage contribute to approximately 31% saving.

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