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Bridge Profile and Vehicle Property Determination Using Vehicle fleet monitoring Concept

INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS 2024年第0期

作者： Ren, Yifei Feng, Kun Obrien, Eugene J. Li, Wanheng He, Haifang Minist Transport Res Inst Highway Beijing Peoples R China Anglia Ruskin Univ Sch Engn & Built Environm Chelmsford England Univ Coll Dublin Sch Civil Engn Dublin Ireland

Over the past 20 years, drive-by monitoring, using data from sensors in passing vehicles, has become increasingly popular due to its low cost and high efficiency at a network level. Despite significant advances, the dynamics of vehicles remains one of the main challenges to overcome before accurately identifying critical information about the infrastructure being crossed, bridges or road pavements. This paper introduces a novel approach to estimating bridge profiles (i.e. road surface profiles on the bridges) and vehicle properties using a fleet of passing vehicles. In this method, bridge profiles are first calculated using the innovative Inverse Newmark-beta integration method, and the cross-entropy optimization algorithm is employed to solve the problem. Numerical results demonstrate that the proposed approach is highly effective in extracting bridge profiles and predicting vehicle properties, even in scenarios with significant levels of measurement noise.

关键词： Bridge structural health monitoring vehicle bridge interaction drive-by fleet monitoring

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Drive-By fleet monitoring to Detect Bearing Damage in Bridges Using a Moving Reference Influence Function

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INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS 2023年第16N18期23卷

作者： OBrien, Eugene J. McCrum, Daniel P. Wang, Shuo Univ Coll Dublin Sch Civil Engn Dublin Ireland

This paper introduces a new bridge damage indicator, the moving reference influence function (MRIF), to detect bridge bearing damage using deflections inferred from vehicle accelerations. Recently, vehicle acceleration has been used to find the apparent profile (AP) of a bridge when a vehicle passes. This AP consists of bridge profile elevations and bridge deflection components. To describe the relationship between these deflection components and load, a MRIF is proposed for the first time in this paper. An error minimization process is used to find the MRIF and the road surface profile on the bridge. The vehicle acceleration signals used in the paper are assumed to be collected from a partially instrumented vehicle fleet. In the fleet, only the first axle acceleration is collected from each vehicle. To simplify the minimization process, both the MRIF and the bridge profile are represented by kernel density functions. The results show that the bridge profile can be accurately obtained and that bridge bearing damage can be identified from the MRIF. Both area and skewness of the MRIF are damage sensitive and can be used together to find the location and severity of bridge bearing damage.

关键词： Drive-by vehicle acceleration fleet monitoring moving reference influence function bridge bearing damage damage detection kernel density function

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A conformal anomaly detection based industrial fleet monitoring framework: A case study in district heating

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期201卷

作者： Farouq, Shiraz Byttner, Stefan Bouguelia, Mohamed-Rafik Gadd, Henrik Halmstad Univ Halmstad Sweden

The monitoring infrastructure of an industrial fleet can rely on the so-called unit-level and subfleet-level models to observe the behavior of a target unit. However, such infrastructure has to confront several challenges. First, from an anomaly detection perspective of monitoring a target unit, unit-level and subfleet-level models can give different information about the nature of an anomaly, and which approach or level model is appropriate is not always clear. Second, in the absence of well-understood prior models of unit and subfleet behavior, the choice of a base model at their respective levels, especially in an online/streaming setting, may not be clear. Third, managing false alarms is a major problem. To deal with these challenges, we proposed to rely on the conformal anomaly detection framework. In addition, an ensemble approach was deployed to mitigate the knowledge gap in understanding the underlying data-generating process at the unit and subfleet levels. Therefore, to monitor the behavior of a target unit, a unit-level ensemble model (ULEM) and a subfleet-level ensemble model (SLEM) were constructed, where each member of the respective ensemble is based on a conformal anomaly detector (CAD). However, since the information obtained by these two ensemble models through their p-values may not always agree, a combined ensemble model (CEM) was proposed. The results are based on real-world operational data obtained from district heating (DH) substations. Here, it was observed that CEM reduces the overall false alarms compared to ULEM or SLEM, albeit at the cost of some detection delay. The analysis demonstrated the advantages and limitations of ULEM, SLEM, and CEM. Furthermore, discords obtained from the state-of-the-art matrix-profile (MP) method and the combined calibration scores obtained from ULEM and SLEM were compared in an offline setting. Here, it was observed that SLEM achieved a better overall precision and detection delay. Finally, the different com

关键词： Conformal anomaly detection fleet monitoring Unit-level model Subfleet-level model Ensemble model District heating substations

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Micro Gas Turbines in the Future Smart Energy System: fleet monitoring, Diagnostics, and System Level Requirements

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FRONTIERS IN MECHANICAL ENGINEERING-SWITZERLAND 2021年 7卷

作者： Aslanidou, Ioanna Rahman, Moksadur Zaccaria, Valentina Kyprianidis, Konstantinos G. Malardalen Univ Sch Innovat Design & Engn Dept Innovat & Prod Realisat Eskilstuna Sweden Malardalen Univ Dept Automat Energy & Environm Engn Vasteras Sweden

The energy generation landscape is changing, pushed by stricter regulations for emissions control and green energy generation. The limitations of renewable energy sources, however, require flexible energy production sources to supplement them. Micro gas turbine based combined heat and power plants, which are used for domestic applications, can fill this gap if they become more reliable. This can be achieved with the use of an engine monitoring and diagnostics system: real-time engine condition monitoring and fault diagnostics results in reduced operating and maintenance costs and increased component and engine life. In order to allow the step change in the connection of small engines to the grid, a fleet monitoring system for micro gas turbines is required. A proposed framework combines a physics-based model and a data-driven model with machine learning capabilities for predicting system behavior, and includes a purpose-developed diagnostic tool for anomaly detection and classification for a multitude of engines. The framework has been implemented on a fleet of micro gas turbines and some of the lessons learned from the demonstration of the concept as well as key takeaways from the general literature are presented in this paper. The extension of fleet monitoring to optimal operation and production planning in relation to the needs of the grid will allow the micro gas turbines to fit in the future green energy system, connect to the grid, and trade in the energy market. The requirements on the system level for the widespread use of micro gas turbines in the energy system are addressed in the paper. A review of the current solutions in fleet monitoring and diagnostics, generally developed for larger engines, is included, with an outlook into a sustainable future.

关键词： micro gas turbine fleet monitoring diagnostics smart energy system production planning automation predictive maintenance

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Reliability approach applied on fatigue safety factors for fleet monitoring 30th

Reliability approach applied on fatigue safety factors for f...

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30th Symposium of the International Committee on Aeronautical Fatigue, ICAF 2019

作者： Montlahuc, Vincent DGA Aeronautical Systems 47 rue Saint-Jean Balma31131 France

ISBN: (纸本)9783030215026

French armament procurement agency (DGA) is currently implementing a reliability approach in order to check aircraft safety level. The goal of the method is to carry out a quantitative study about the structural failure based on random phenomenon. Physical randomness is considered in the aviation regulation with the use of scatter factors. Dealing with safe-life substantiated aircraft, DGA, in charge of airworthiness, allows the reduction from 5 to 3 of the fatigue scatter factor for the monitored fleets with a safety level at least preserved. Two aspects are analyzed with the reliability method: The first application evaluates the safety level induced by different monitoring systems. The second one quantifies the safety level of aircraft without monitoring system compared with monitored ones in order to evaluate the reduce scatter factor. © Springer Nature Switzerland AG 2020.

关键词： Fatigue fleet monitoring Reliability Safe-life Safety factor Scatter factor

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Fatigue crack growth approach for fleet monitoring 30th

Fatigue crack growth approach for fleet monitoring

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30th Symposium of the International Committee on Aeronautical Fatigue, ICAF 2019

作者： Gillet, Olivier Bayart, Bastien DGA Aeronautical Systems 47 Rue Saint Jean – BP 93123 Balma Cedex31131 France

ISBN: (纸本)9783030215026

Aircraft are subjected to unpredictable variable amplitude loadings (operational loads, varying widely according to the missions flown by the aircraft). For safety reasons, it is necessary to estimate each aircraft’s actual fatigue "consumption" relative to its potential. For this purpose, most French military aircraft are equipped with load monitoring systems, giving direct or indirect access to the in-service loads. The cumulative fatigue damage is calculated at various points of the structure pointed out as being critical. Thus, for fleet life-extension purposes and usage predictions, the use of more precise damage and crack growth prediction models is a major concern. The initiation models are yet insufficient to cover the entire aircraft lifetime. In order to maintain the design safety margin, the airworthiness authority introduced the damage tolerance philosophy: cracks are allowed below a given size provided that the propagation process is well known. Systems currently in operation on the French Air Force enable the acquisition of large number of flight parameters. This data associated with crack growth models provide information about the propagation phase. The fatigue potential consumption during the operational life can thus be estimated by combining initiation and propagation models. Initiation model are based either on real load sequence combined with unit damage matrix or on rebuilt load sequence (from g-counters output) combined with cumulative damage laws. Different propagation models are investigated. At this stage, experiments have been carried out on CT samples and are ongoing on a geometry close to the spar one (thick holed aluminum alloy part). Further investigations will be carried out to extend the conclusions made on aircraft equipped with load monitoring systems to aircrafts only equipped with g-counters. It means build a temporal spectrum using g-counters output in order to take load history into account in the propagation process (mostly re

关键词： Crack growth fleet monitoring Propagation models Variable amplitude loading

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Scalable fleet monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications

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SENSORS 2020年第15期20卷 4308.-4308页

作者： Moens, Pieter Bracke, Vincent Soete, Colin Vanden Hautte, Sander Avendano, Diego Nieves Ooijevaar, Ted Devos, Steven Volckaert, Bruno Van Hoecke, Sofie Ghent Univ imec IDLab Technol Pk Zwijnaarde 122 B-9052 Ghent Belgium Flanders Make Corelab Decis Celestijnenlaan 300 B-3001 Leuven Belgium

The wide adoption of smart machine maintenance in manufacturing is blocked by open challenges in the Industrial Internet of Things (IIoT) with regard to robustness, scalability and security. Solving these challenges is of uttermost importance to mission-critical industrial operations. Furthermore, effective application of predictive maintenance requires well-trained machine learning algorithms which on their turn require high volumes of reliable data. This paper addresses both challenges and presents the Smart Maintenance Living Lab, an open test and research platform that consists of a fleet of drivetrain systems for accelerated lifetime tests of rolling-element bearings, a scalable IoT middleware cloud platform for reliable data ingestion and persistence, and a dynamic dashboard application for fleet monitoring and visualization. Each individual component within the presented system is discussed and validated, demonstrating the feasibility of IIoT applications for smart machine maintenance. The resulting platform provides benchmark data for the improvement of machine learning algorithms, gives insights into the design, implementation and validation of a complete architecture for IIoT applications with specific requirements concerning robustness, scalability and security and therefore reduces the reticence in the industry to widely adopt these technologies.

关键词： fleet monitoring bearing degradation Cyber-Physical System predictive maintenance Industrial Internet of Things Industry 4 0 accelerated lifetime testing

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A Dynamic Dashboarding Application for fleet monitoring Using Semantic Web of Things Technologies

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SENSORS 2020年第4期20卷 1152-1152页

作者： Vanden Hautte, Sander Moens, Pieter Van Herwegen, Joachim De Paepe, Dieter Steenwinckel, Bram Verstichel, Stijn Ongenae, Femke Van Hoecke, Sofie Univ Ghent IDLab IMEC Technol Pk Zwijnaarde 122 B-9052 Ghent Belgium

In industry, dashboards are often used to monitor fleets of assets, such as trains, machines or buildings. In such industrial fleets, the vast amount of sensors evolves continuously, new sensor data exchange protocols and data formats are introduced, new visualization types may need to be introduced and existing dashboard visualizations may need to be updated in terms of displayed sensors. These requirements motivate the development of dynamic dashboarding applications. These, as opposed to fixed-structure dashboard applications, allow users to create visualizations at will and do not have hard-coded sensor bindings. The state-of-the-art in dynamic dashboarding does not cope well with the frequent additions and removals of sensors that must be monitored-these changes must still be configured in the implementation or at runtime by a user. Also, the user is presented with an overload of sensors, aggregations and visualizations to select from, which may sometimes even lead to the creation of dashboard widgets that do not make sense. In this paper, we present a dynamic dashboard that overcomes these problems. Sensors, visualizations and aggregations can be discovered automatically, since they are provided as RESTful Web Things on a Web Thing Model compliant gateway. The gateway also provides semantic annotations of the Web Things, describing what their abilities are. A semantic reasoner can derive visualization suggestions, given the Thing annotations, logic rules and a custom dashboard ontology. The resulting dashboarding application automatically presents the available sensors, visualizations and aggregations that can be used, without requiring sensor configuration, and assists the user in building dashboards that make sense. This way, the user can concentrate on interpreting the sensor data and detecting and solving operational problems early.

关键词： dynamic dashboards Semantic Web of Things Industry 4.0 fleet monitoring SSN ontology Web Thing Model

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Transformer fleet monitoring 4th

Transformer Fleet Monitoring

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4th International Colloquium on Transformer Research and Asset Management

作者： Jakovic, Tihomir Murat, Ivan Klaric, Filip Keitoue, Samir Fallerovo Setaliste 22 Zagreb 10002 Croatia

Transformer monitoring systems are increasing in number and having a clear overview of the whole fleet of monitored transformers is becoming crucial in an effort to provide the end user with an easy way to monitor its transformer assets and react accordingly to the monitored situations. This paper will introduce a fleet monitoring solution that provides the architecture and tools to accomplish the set tasks of connecting and accessing information of all the monitoring systems from their dedicated locations. Standard transformer monitoring system configuration is made of a local unit on the transformer for collecting and processing raw data from the sensors and transferring the data to another unit, usually a server located in the substation which stores it in the database. The server unit is also responsible for the presentation of data to the end user and communication with supervisory control systems. This configuration is well capable of achieving its purpose of monitoring the transformer state and operation. The challenge is in accessing the monitored data. Traditional approach for accessing the data was to access the servers locally in the substations. Responding to the monitored conditions was done by connecting critical alarm signals from the monitoring system to the substation control system and in case of an alarm indication the operator from the substation was required to access the monitoring system for further details and inform personnel responsible for the maintenance of transformer units. The transformer maintenance personnel were then required to check the monitored data either by connecting remotely to the monitoring system, which in most cases is not possible due to security and technical restrictions, or to travel to the remote location to investigate the issue. This approach was both very time consuming and costly. There is also a consideration that the modern substations are becoming unmanned or have just a basic crew which may consist of person

关键词： transformer monitoring fleet monitoring asset management

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Transformer fleet monitoring

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Procedia Engineering 2017年 202卷 20-28页

作者： Tihomir Jaković Ivan Murat Filip Klarić Samir Keitoue Tihomir Jaković Fallerovo šetalište 22 10002 Croatia Ivan Murat Fallerovo šetalište 22 10002 Croatia

关键词： transformer monitoring fleet monitoring asset management

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