This paper explores stochastic maintenance for a large fleet of structures, focusing on the example of bridges. The approach involves replacing the states of degradation of a facility with a probability distribution over the states. Even though the degradation state of each individual structure may be available, it is often more convenient to work with the proportions of structures in each degradation state. This is particularly useful when incorporating constraints such as limited maintenance budgets or desired quality levels for the overall fleet. Probability distributions are commonly used when the initial state is unknown or when degradation is observed with error (i.e., under partial observation). However, the same methodology is applied here in a different context. Full information may be available, but using it directly can be too complex. Instead, partial information is used to reduce this complexity. The theory of Markov Decision Processes (MDPs) provides a framework for many applications in Operations Research and Management Science, and stochastic maintenance has become one such application. When working with probability distributions over states instead of individual states, the framework is referred to as a mean-field MDP. In this setting, the dynamic programming methodology for MDPs is extended to the mean-field case, tailored to fleets of structures. Both value iteration and policy iteration algorithms are considered to characterize the value function and determine the optimal (randomized) control policy.

Structural Heath Monitoring (SHM) systems for civil structures are usually designed with the aim to assess damage, following the consecutive levels of damage detection, localization, qualification, quantification, and, if possible, prediction. However, some SHM applications are not directly linked to the risk of occurrence of specific damage, but to the global performance of the civil structure to withstand the loads it was designed for. Thus, the measurements gathered by the SHM system can be distilled into Health Indicators (HI), the evolution of which through time shall provide an insight into the load effects and the capacity of the asset to sustain them, independently from the task of damage assessment. The definition of such Health Indicators may vary depending on the civil asset, the expected loads and the operational context. Practical feedback is given from three operational case studies, involving continuous strain and vibration measurements over several years. In the Mont-Blanc Tunnel linking France to Italy through the Alps, the HI were computed from strain measurements of the intrados of the concrete slab which supports the road lanes inside the tunnel. The Vière Bridge in the French Alps, a masonry arch that withstands the effects from soil movement at its abutments, has been monitored with wireless strain sensors for eight years, using another definition of HI to track its evolution. In Greece, the Smart Bridges project involves the monitoring of a large portfolio of 250 bridges across the whole country, requiring a dedicated HI design to allow for comparative analysis among different bridges monitored with similar sets of strain and acceleration sensors. The data analysis concepts used by each HI to manage the large quantity of raw measurement data collected are discussed, as well as some operational decisions of asset management, to which the Health Indicators released by the SHM eventually contributed.

A new method has been proposed to analyze acoustic emission (AE) signals obtained from a prestressed concrete viaduct to realize the cracking mechanisms that evolve during the passage of trucks/cars. This new method exploits the information contained in narrow partial powers (PPs) bands of the acquired AE signals. The narrow PP bands are found to be extremely sensitive to the evolving damage modes in concrete, both in the case of in situ and laboratory experiments. Results show that the classification of trucks is possible using the cracking information contained in the narrow PP bands of the acquired AE signals during the passage of trucks. AE-monitored laboratory bending experiments have been performed on reinforced concrete T-beams. The effectiveness of the proposed narrow PP bands in the discrimination of damage modes in the beams is found to be overwhelming. The proposed method has a unique advantage in visualizing the frequency content of a large number of AE signals by using the narrow PP-based heatmap. This study consists of simultaneous measurement of strain and AE during the passage of trucks/cars, and correspondence between the two types of data has also been presented. This research work shows that frequency information of AE signals can be used for monitoring of prestressed/RC concrete structures.

Industry 4.0 (I4.0) represents a transformative approach, integrating technology, production methods, and information and communication technology to enhance industrial value creation. A central I4.0 goal in the energy domain is improving energy efficiency to boost industrial competitiveness and profitability. Given that induction motors account for nearly two-thirds of industrial electrical energy consumption, optimizing their efficiency is crucial. Energy management systems (EMSs) need real-time data to assess motor efficiency, enabling prompt identification and replacement of inefficient motors with alternatives that have optimal efficiency class and rated power for specific applications. This paper introduces a novel non-intrusive method for estimating the load and efficiency of induction motors without disrupting their operation. To reach that goal, the proposed method optimizes the parameters of a set of relationships between output power, input power, and losses with the motor speed, minimizing the error in the estimates. It requires only input electrical power and motor speed measurements to set the model parameters and estimates the load and efficiency using either speed or input power measurements. The experimental results demonstrate that the proposed method, with a mean overall error of less than 3.5% in estimating output power and efficiency, outperforms conventional methods.

Operational Modal Analysis (OMA) is widely used in the frame of Structural Health Monitoring (SHM) to assess the dynamic properties of civil structures from continuous measurements. The results, given as natural vibration frequencies, damping ratios and mode shapes, can be compared to the theoretical results of a Finite Elements Model (FEM) of the structure. In case of differences, some parameters might be tuned to update the model and make it fit to the observed reality of the structure. Moreover, changes in the modal properties can be used to detect anomalies, and precisely assess them by a new update of the model. This method has been recently applied on two historical iron truss bridges spanning the river Loire in France, in the frame of the "GeRICO" research project, as part of the "Programme National Ponts". This contribution presents the results and feedback of this activity. The monitoring system is described first: one of the two bridges was equipped with a comprehensive set of accelerometers, to distinguish modes of bending, torsion, and distortion of the iron truss box girder. In addition, longbasis strain sensors were included in a comparative analysis to assess their ability to record bending vibration modes. The second bridge had a partial monitoring, on two spans out of eleven only, with a reduced number of accelerometers, to optimize the quantity of sensors. The datasets used for the OMA are discussed: an interesting result is the possibility to use concatenated, short trigger-based records, instead of continuous hour-long ambient noise records, without significant loss of information. Secondly, the link with the FEM is established by the means of advanced correlation analysis between measured and theoretical modal properties. The way to use this correlation to update the FEM is investigated.

This work presents an acoustic emission (AE) based method, named a series of narrow partial power bands (SN2PB), to monitor the damage mechanisms within reinforced concrete beams during quasi-static bending tests. Unlike conventional time-domain methods, which give a global view of the involved cracking modes, SN2PB has the advantage of obtaining information on cracking modes within each AE hit in reduced frequency bands. SN2PB is applied by dividing the frequency content of each AE signal into narrow bands. Results show that the same AE signal can contain shear and tension cracking signatures at the lower and upper frequency bands, respectively. This work shows also the presence of a transitional domain between the two distinct bands. Changes and fluctuations corresponding to the involved mechanisms during the entire mechanical tests are therefore followed and visualized in the form of a heatmap. Moreover, the densities of the shear and tensile mechanisms at an instant are also determined using a Gaussian Mixture Model. Results show that the separation obtained using the SN2PB method is more advantageous than that of the conventional method based on the average frequency and the RA parameter.

Increasing loads, ageing of materials as well as environmental change make the issue of fatigue damage critical on bridges, especially in the case of old steel or iron structures. The assessment of the fatigue loads on specific elements of these assets can be carried out from on-field strain measurements with the application of classical methods involving rainflow cycle counting and the use of Palmgren-Miner's rule. However, such a fatigue assessment suffers of significant uncertainties about the real fatigue damage ratio and the consecutive residual fatigue life. A new probabilistic method for the fatigue assessment is proposed, as the result of a joint research by OSMOS Group and the Ecole des Ponts. The theoretical background of the method is described, involving a probabilistic formulation of Miner's rule and the use of a Weibull-Basquin model to describe the health of structural elements. Real field case studies are presented, on two historical iron truss road bridges in France, with a dataset of more than 18 months of continuous strain measurements used for the fitting of the probabilistic model of fatigue loads. The incidence of the quantity of data and of the number of sensors used for the analysis is discussed, along with the conclusive results concerning the probabilistic description of the residual fatigue life.

A bridge Weigh-in-Motion (WIM) system consists in estimating gross vehicle weight while they cross a bridge, without any sensor in the pavement. It comprises a set of sensors installed under the bridge deck and in bridge elements, and dedicated data analysis tools, allowing to assess the moving loads from the mechanical response of the bridge structure. In this paper, a novel Bridge WIM solution is described, which uses a low number of optical strands strain sensors, and has been implemented on 13 bridges worldwide. The accuracy of this system for gross vehicle weight is assessed on two bridges in France and Italy. The system met the accuracy class A(5) of the COST323 specifications on one bridge. Moreover, the data collected by the system is useful also for the structural health monitoring of the bridge.

Structural Health Monitoring (SHM) systems have been widely used in the last years as integrated tools for the management of civil infrastructures. Some SHM projects now reach significant durations of several years, which induces interesting challenges when they imply numerous sensors with high sampling rates. This paper aims to present feedback on a few real cases of long time SHM on civil structures, performed by OSMOS Group as a SHM service provider. The examples include various types of structures, with concrete, masonry, and metallic bridges as well as tunnels, and address the cases of both wired and wireless autonomous SHM systems, some of them with more than ten years of operation. Issues related to the data flow and storage are described, which represent high volumes of data, since the SHM systems are aimed to assess continuously the dynamic response of the structures with 100 measurements every second. Maintenance and upgrading of the SHM systems along such important durations are addressed also. Finally, the discussion focuses on massive data analysis tools dedicated to long-time SHM, which enable to reduce the amount of raw data into useful synthetic information for anomaly detection and prognosis.

This paper describes a new configuration of Cuk and SEPIC (Single-Ended Primary Converter) ZVS-QR (zero-voltage switching quasi-resonant) combination DC-DC converter for bipolar output with a single switch. The proposed topology employs a single ground-referenced power switch, which simplifies the gate drive design with a single L-C resonant network and provides a bipolar output voltage with good regulation, acceptable efficiency and a step-down/up conversion ratio. This configuration provides dual-output voltage by switching the power switch to zero voltage, which is an interesting alternative for many applications where small size, light weight and high power density are very important aspects. In order to verify its performance, a SEPIC–Cuk Combination ZVS-QR prototype with a cost-effective commercial resonant controller was designed and tested. The experimental results show that the proposed combined topology is suitable for Single-Input Dual-Output (SIDO) applications.

Human-machine environments require computers with capacity to store large amounts of data, and it’s important that memories do not fail, otherwise it will jeopardize the Human-machine interface. Moreover, as battery operated devices are becoming widely used in everyday Human-Machine environments, it’s also imperative to reduce power consumption in these devices. The present work presents a new DRAM performance sensor, to be used in DRAM memories of Human-Machine environments, especially in battery operated devices. Effects such as process variations (P), power-supply voltage variations (V), temperature variations (T) and aging (A) variations (PVTA – Process, Voltage, Temperature and Aging) are key parameters that affect chips performance and reliability. The new performance sensor for DRAM memories has the purpose to signalize when these PVTA variations, or any other parameter, change performance of the memory above a certain threshold limit, jeopardizing memory operation, signal integrity, and the Human-Machine system where it is used. Sensor’s sensibility to PVTA variations can be changed in run-time, which allows the sensor to be tuned during circuit’s life time. Another important feature is that it can be applied locally, to monitor the online operation of the memory, or globally, by monitoring a dummy memory in pre-defined conditions. These features allow the development of intelligent hardware to be used in Human-Machine systems which allow anticipating system failures and also improve power optimization. Moreover, as far as authors know, this is the first online performance sensor for DRAM memories.

In many countries, renewable energy production already represents an important percentage of the total energy that is generated in electrical grids. In order to reach higher levels of integration, demand side management measures are yet required. In fact, different from the legacy electrical grids, where at any given instant the generation levels are adjusted to meet the demand, when using renewable energy sources, the demand must be adapted in accordance with the generation levels, since these cannot be controlled. In order to alleviate users from the burden of individual control of each appliance, energy management systems (EMSs) have to be developed to both monitor the generation and consumption patterns and to control electrical appliances. In this context, the main contribution of this chapter is to present the implementation of such an IoT-based monitoring and control system for microgrids, capable of supporting the development of an EMS.

Multiple-output DC–DC converters are essential in a multitude of applications where different DC output voltages are required. The interest and importance of this type of multiport configuration is also reflected in that many electronics manufacturers currently develop integrated solutions. Traditionally, the different output voltages required are obtained by means of a transformer with several windings, which are in addition to providing electrical isolation. However, the current trend in the development of multiple-output DC–DC converters follows general aspects, such as low losses, high-power density, and high efficiency, as well as the development of new architectures and control strategies. Certainly, simple structures with a reduced number of components and power switches will be one of the new trends, especially to reduce the size. In this sense, the incorporation of devices with a Wide Band Gap (WBG), particularly Gallium Nitride (GaN) and Silicon Carbide (SiC), will establish future trends, advantages, and disadvantages in the development and applications of multiple-output DC–DC converters. In this paper, we present a review of the most important topics related to multiple-output DC–DC converters based on their main topologies and configurations, applications, solutions, and trends. A wide variety of configurations and topologies of multiple-output DC–DC converters are shown (more than 30), isolated and non-isolated, single and multiple switches, and based on soft and hard switching techniques, which are used in many different applications and solutions.

The first major step in training an object detection model to different classes from the available datasets is the gathering of meaningful and properly annotated data. This recurring task will determine the length of any project, and, more importantly, the quality of the resulting models. This obstacle is amplified when the data available for the new classes are scarce or incompatible, as in the case of fish detection in the open sea. This issue was tackled using a mixed and reversed approach: a network is initiated with a noisy dataset of the same species as our classes (fish), although in different scenarios and conditions (fish from Australian marine fauna), and we gathered the target footage (fish from Portuguese marine fauna; Atlantic Ocean) for the application without annotations. Using the temporal information of the detected objects and augmented techniques during later training, it was possible to generate highly accurate labels from our targeted footage. Furthermore, the data selection method retained the samples of each unique situation, filtering repetitive data, which would bias the training process. The obtained results validate the proposed method of automating the labeling processing, resorting directly to the final application as the source of training data. The presented method achieved a mean average precision of 93.11% on our own data, and 73.61% on unseen data, an increase of 24.65% and 25.53% over the baseline of the noisy dataset, respectively.

Weigh-in-Motion is currently the only way to precisely assess and monitor traffic loads on road bridges from real measurements. This assessment helps to detect potential overweight vehicles and to optimize the maintenance operations on the bridge thanks to an accurate knowledge of its real load conditions.An experiment, performed on a precast prestressed concrete beam girders bridge overcrossing a highway in France, is described. The Weigh-in-Motion (WIM) system uses the bridge deck as a large scale, part of the weighing device, and measures strain in critical parts of the structure.The system is able to get significantly accurate estimations of the gross weight of the vehicles on most types of bridges, including long span box girders, large composite decks or the multiple precast prestressed concrete beams considered in the study. However, the axle load estimation is still much less accurate and not presented here.The experiment started in February 2019 and is still going on, also proving the robustness of the solution for an operation over long durations, as a permanent part of the bridge management through its whole lifecycle. Thus, the WIM sensors used are relevant for the Structural Health Monitoring of the bridge deck as well.

Energy consumption and, consequently, the associated costs (e.g., environmental and monetary) concern most individuals, companies, and institutions. Platforms for the monitoring, predicting, and optimizing energy consumption are an important asset that can contribute to the awareness about the ongoing usage levels, but also to an effective reduction of these levels. A solution is to leave the decisions to smart system, supported for instance in machine learning and optimization algorithms. This chapter involves those aspects and the related fields with emphasis in the prediction of energy consumption to optimize its usage policies.

Bipolar DC grids have become an adequate solution for high-power microgrids. This is mainly due to the fact that this configuration has a greater power transmission capacity. In bipolar DC grids, any distributed generation system can be connected through DC-DC converters, which must have a monopolar input and a bipolar output. In this paper, a DC-DC converter based on the combination of single-ended primary-inductor converter (SEPIC) and Ćuk converters is proposed, to connect a photovoltaic (PV) system to a bipolar DC grid. This topology has, as main advantages, a reduced number of components and a high efficiency. Furthermore, it can contribute to regulate/balance voltage in bipolar DC grids. To control the proposed converter, any of the techniques described in the literature and applied to converters of a single input and single output can be used. An experimental prototype of a DC-DC converter with bipolar output based on the combination of SEPIC and Ćuk converters was developed. On the other hand, a perturb and observe method (P and O) has been applied to control the converter and has allowed maximum power point tracking (MPPT). The combined converter was connected in island mode and in parallel with a bipolar DC microgrid. The obtained results have allowed to verify the behavior of the combined converter with the applied strategy.

Knowing the actual effects of traffic and temperature on a bridge and its consequences in terms of stress cycles in the bridge structure is of great value in the scheme of a resilient asset management. A solution is proposed in the case of different types of road bridges in Europe, based on continuous strain monitoring by the mean of Optical Strands sensors and of dedicated analysis tools provided by OSMOS Group. The choice of performing continuous strain measurements on critical parts of the bridge is discussed, as a relevant solution in order to provide the control of the actual effects of traffic and temperature on the structure and the assessment of the structural elements in terms of strain and stress, both under the effects of the live loads and over the long term. As the monitoring device is conceived as a permanent solution for these bridges, the accumulated data over several months allow a statistical analysis of the effects of heavy traffic and relevant anomaly detection from several criteria at different time scales: dynamic behavior, stability of the response to temperature changes, long-term stability under the effects of the dead load after thermal correction. The monitoring of bridges through continuous high-sampled strain measurements over long periods as proposed by OSMOS is an integrated solution which answers to several different problematics, both for the daily management through detection of overweight vehicles, and for the long-term assessment through lifespan estimation and anomaly detection.

Ultra-low-power strategies have a huge importance in today's integrated circuits designed for internet of everything (IoE) applications, as all portable devices quest for the never-ending battery life. Dynamic voltage and frequency scaling techniques can be rewarding, and the drastic power savings obtained in subthreshold voltage operation makes this an important technique to be used in battery-operated devices. However, unpredictability in nanoscale chips is high, and working at reduced supply voltages makes circuits more vulnerable to operational-induced delay-faults and transient-faults. The goal is to implement an adaptive voltage scaling (AVS) strategy, which can work at subthreshold voltages to considerably reduce power consumption. The proposed strategy uses aging-aware local and global performance sensors to enhance reliability and fault-tolerance and allows circuits to be dynamically optimized during their lifetime while prevents error occurrence. Spice simulations in 65nm CMOS technology demonstrate the results.

Human-Computer Interaction (HCI) applications need reliable hardware and the development of today’s sensors and cyber-physical systems for HCI applications is critical. Moreover, such hardware is becoming more and more self-powered, and mobile devices are today important devices for HCI applications. While battery-operated devices quest for the never-ending battery, aggressive low-power techniques are used in today’s hardware systems to accomplish such mission. Techniques like Dynamic Voltage and Frequency Scaling (DVFS) and the use of subthreshold power-supply voltages can effectively achieve substantial power savings. However, working at reduced power-supply voltages, and reduced clock frequency, imposes additional challenges in the design and operation of devices. Today’s chips face several parametric variations, such as PVTA (Process, power-supply Voltage, Temperature and Aging) variation, which can affect circuit performance and reliability is affected. This paper presents a performance sensor solution to be used in cyber-physical systems to improve reliability of today’s chips, guaranteeing an error-free operation, even with the use of aggressive low-power techniques. In fact, this performance sensor allows optimize the trade-off between power and performance, avoiding the occurrence of errors. In order to be easily used and adopted by industry, the performance sensor is a non-intrusive global sensor, which uses two dummy critical paths to sense performance for the power-supply voltage and clock frequency used, and for the existing PVTA variation. The novelty of this solution is on the new architecture for the sensor, which allows the operation at VDDs’ subthreshold voltage levels. This feature makes this global sensor a unique solution to control DVFS, even at subthreshold voltages, avoid performance errors and allow optimizing circuit operation and performance. Simulations using a SPICE tool allowed characterizing the new sensor to work at sub-threshold voltages, and results are presented for a 65 nm CMOS technology, which uses a CMOS Predictive Technology Models (PTM) technology. The results show that the sensor increases sensibility when PVTA degradations increase, even when working at subthreshold voltages.