Conferences related to Sensors For Condition-based Maintenance

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2020 59th IEEE Conference on Decision and Control (CDC)

The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.


2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.


2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

The Conference focuses on all aspects of instrumentation and measurement science andtechnology research development and applications. The list of program topics includes but isnot limited to: Measurement Science & Education, Measurement Systems, Measurement DataAcquisition, Measurements of Physical Quantities, and Measurement Applications.


2020 IEEE Power & Energy Society General Meeting (PESGM)

The Annual IEEE PES General Meeting will bring together over 2900 attendees for technical sessions, administrative sessions, super sessions, poster sessions, student programs, awards ceremonies, committee meetings, tutorials and more


IECON 2020 - 46th Annual Conference of the IEEE Industrial Electronics Society

IECON is focusing on industrial and manufacturing theory and applications of electronics, controls, communications, instrumentation and computational intelligence.


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Periodicals related to Sensors For Condition-based Maintenance

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Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


Automation Science and Engineering, IEEE Transactions on

The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...


Dielectrics and Electrical Insulation, IEEE Transactions on

Electrical insulation common to the design and construction of components and equipment for use in electric and electronic circuits and distribution systems at all frequencies.


Display Technology, Journal of

This publication covers the theory, design, fabrication, manufacturing and application of information displays and aspects of display technology that emphasize the progress in device engineering, device design, materials, electronics, physics and reliabilityaspects of displays and the application of displays.


Industrial Electronics, IEEE Transactions on

Theory and applications of industrial electronics and control instrumentation science and engineering, including microprocessor control systems, high-power controls, process control, programmable controllers, numerical and program control systems, flow meters, and identification systems.


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Most published Xplore authors for Sensors For Condition-based Maintenance

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Xplore Articles related to Sensors For Condition-based Maintenance

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Automated scheduling using Condition Based Maintenance

2011 IEEE Conference on Prognostics and Health Management, 2011

Maintenance scheduling of machines and their various components can be logistically challenging for a firm or factory. Traditional maintenance schedules are produced using time based preventive maintenance guidelines. But the preventive maintenance is imprecise as a machine might need a repair before the scheduled timeline due to over-use or premature failure of certain parts. Preventive Maintenance also may cause unneeded ...


Low-Cost Vibration Sensor for Condition-Based Monitoring Manufactured From Polyurethane Foam

IEEE Sensors Letters, 2017

Ubiquitous vibration sensing forms a core requirement of Internet of Things (IoT) applications in condition-based monitoring (CbM). Such sensors can enable cost savings by identifying incipient failures in industrial machinery and, thereby, optimized maintenance schedule planning. Conventional piezoelectric and microelectromechanical systems (MEMS)-based vibration sensors developed for such applications cost upwards of tens and hundreds of dollars, limiting the scale of ...


Sensor-Driven Condition-Based Generator Maintenance Scheduling—Part I: Maintenance Problem

IEEE Transactions on Power Systems, 2016

Traditionally, generator maintenance scheduling has been implemented using highly conservative maintenance policies based on manufacturing specifications and engineering expertise on the type of generators. However, recent advances in sensor technology, signal processing, and embedded online diagnosis provide more unit-specific information on the degradation characteristics of the generators. In this two-paper study, we propose a new generation maintenance framework that integrates ...


Design of an automated vibration monitoring system for condition based maintenance of a lathe machine (Case study)

2016 International Conference on System Reliability and Science (ICSRS), 2016

This article presents an automated vibration monitoring system for a lathe machine. This study was motivated by the fact that machine production time was wasted during planned maintenance when, most times, the machines did not require any maintenance at all. Also, the periodic intervals used did not depict the correct ageing of the machine components which resulted in unexpected failure ...


Developing Diagnostics and Prognostics of Data Center Systems Implementing with Condition-Based Maintenance

IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, 2018

The condition-based maintenance (CBM) focuses on the prediction of aging, degradation, and failure process of data center at the levels of components and systems. The benefits of CBM are increasing system availability, mission effectiveness, and reducing maintenance costs. In this paper, we propose an innovative concept of decision support methodology for system failure diagnosis and prognosis in complex systems of ...


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Educational Resources on Sensors For Condition-based Maintenance

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IEEE.tv Videos

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IEEE-USA E-Books

  • Automated scheduling using Condition Based Maintenance

    Maintenance scheduling of machines and their various components can be logistically challenging for a firm or factory. Traditional maintenance schedules are produced using time based preventive maintenance guidelines. But the preventive maintenance is imprecise as a machine might need a repair before the scheduled timeline due to over-use or premature failure of certain parts. Preventive Maintenance also may cause unneeded repair of parts that still have remaining useful life. Condition Based Maintenance (CBM) is a proactive maintenance approach that takes into account the real conditions of the parts using sensors and then offers guidelines to predict the functional failure ahead. The automated scheduling model that we describe here takes the CBM input into account along with the preventive maintenance guidelines, availability of parts, facilities and numerous other constraints to come up with optimum maintenance schedule. The automated scheduler is developed using Gecode based on Finite Domain Constraint paradigm that can take multiple constraints to model the various eccentricities of the scheduling problem. Other logistical support systems (e.g., ordering of parts, machinists etc) can also be scheduled using the Automated Scheduler alongside with scheduling maintenance of a machine. Since the scheduler can be run anytime, the most deserving candidate will be selected for maintenance at a given time. This will improve efficiency and reduce support cost by repairing the machine that is of urgent need.

  • Low-Cost Vibration Sensor for Condition-Based Monitoring Manufactured From Polyurethane Foam

    Ubiquitous vibration sensing forms a core requirement of Internet of Things (IoT) applications in condition-based monitoring (CbM). Such sensors can enable cost savings by identifying incipient failures in industrial machinery and, thereby, optimized maintenance schedule planning. Conventional piezoelectric and microelectromechanical systems (MEMS)-based vibration sensors developed for such applications cost upwards of tens and hundreds of dollars, limiting the scale of their deployment. In this article, we present an extremely inexpensive vibration sensor prepared with commercially available polyurethane foam that is commonly used for packaging of fragile goods. We present a process to coat the pores of the foam with conductive carbon ink to impart piezo-resistive properties to the material. A proof of concept realization of vibration sensor with 80-Hz sensing bandwidth is presented, along with experimental data demonstrating classification of vibration signals for different machine operating conditions. The spectral content of the measured vibration signal shows good agreement with spectral content of the audio recordings of corresponding acoustic measurements.

  • Sensor-Driven Condition-Based Generator Maintenance Scheduling—Part I: Maintenance Problem

    Traditionally, generator maintenance scheduling has been implemented using highly conservative maintenance policies based on manufacturing specifications and engineering expertise on the type of generators. However, recent advances in sensor technology, signal processing, and embedded online diagnosis provide more unit-specific information on the degradation characteristics of the generators. In this two-paper study, we propose a new generation maintenance framework that integrates the sensor-driven predictive maintenance technologies with optimal maintenance scheduling models. In Part I, we propose a new mixed-integer optimization model for generation maintenance scheduling, which effectively incorporate the dynamic information of generators' health and maintenance cost provided by the Bayesian prognostic models. In Part II, we propose a framework that extends the maintenance model presented herein, and consider the effects of maintenance on network operation by coordinating generator maintenance schedules with the unit commitment and dispatch decisions. We introduce new reformulations and efficient algorithms for solving large-scale instances of the proposed maintenance scheduling model. Extensive computational studies using real-world degradation data demonstrates the effectiveness of the new framework.

  • Design of an automated vibration monitoring system for condition based maintenance of a lathe machine (Case study)

    This article presents an automated vibration monitoring system for a lathe machine. This study was motivated by the fact that machine production time was wasted during planned maintenance when, most times, the machines did not require any maintenance at all. Also, the periodic intervals used did not depict the correct ageing of the machine components which resulted in unexpected failure of the machine. Planned maintenance schedules are done with the assumption that the machine is going to breakdown after a certain period of time. The aim of this research was to come up with a vibration monitoring system for a lathe machine, which included incorporating an electronic circuit in the system, use of liquid crystal display for improved user interface and use of vibration sensors to determine the vibration level of the machine. Experimental research design was used to determine the acceptable ranges of vibration amplitudes in order to classify the amplitude into 4 groups namely: extremely rough, rough, acceptable and smooth. The designed system produced consistent vibration amplitudes for both machining and nonmachining operation. The system used different indicators linked to the main processor of the circuit which monitors the machine real-time performance. It was capable of alerting the user when the vibration amplitude was out of range and also to switch off the machine when the vibration threshold was exceeded. The vibration monitoring system helps in damage control and enables preventive measures to be taken before damage occurs.

  • Developing Diagnostics and Prognostics of Data Center Systems Implementing with Condition-Based Maintenance

    The condition-based maintenance (CBM) focuses on the prediction of aging, degradation, and failure process of data center at the levels of components and systems. The benefits of CBM are increasing system availability, mission effectiveness, and reducing maintenance costs. In this paper, we propose an innovative concept of decision support methodology for system failure diagnosis and prognosis in complex systems of data center power distribution systems. This paper proposes an action research of a new decision support methodology for system failure diagnosis and prognosis in data center power distribution systems. Shifting from time-based maintenance (TBM) to CBM using automated prognostics and diagnostics to identify and resolve issues before they become problems of data center downtime costs.

  • Sensor standards harmonization-path to achiewving sensor interoperability

    Distributed sensor networks are emerging technology for building applications in control and condition monitoring of equipment and machinery in government and industry. Open sensor interfaces, standard sensor data formats, and messaging standards are needed to enable the integration, access, fusion, use, and delivery of sensor-derived data for these applications. The sensor standards harmonization working group was formed at NIST to address these types of issues. This paper examines some relevant open standards that can help to achieve seamless sensor connections, integration, discovery, access, and usage within and across systems, networks, and enterprises through the Web.

  • Energy-autonomous wireless vibration sensor for condition-based maintenance of machinery

    This paper addresses the development of an energy-autonomous wireless vibration sensor for condition-based monitoring of machinery. Such technology plays an increasingly important role in modern manufacturing industry. In this work, energy harvesting is realized by resorting to a custom designed thermoelectric generator. The developed wireless vibration sensor has a remotely tunable sampling rate, which caters to the different needs of various operating conditions. The two key features, energy autonomy and wireless measurement, are demonstrated successfully by the experimental results obtained on the thermoelectric generator and the wireless sensor.

  • Sensor-Driven Condition-Based Generator Maintenance Scheduling—Part II: Incorporating Operations

    A framework for sensor driven condition based generator maintenance scheduling was proposed in Part I of this paper. In Part II, we extend the previous model by incorporating the unit commitment and dispatch into the optimal maintenance scheduling problem. We reformulate this extended maintenance scheduling problem as a two-stage mixed integer program. We use this reformulation to construct an algorithm that obtains the global optimal solution to the proposed generator maintenance problem. Finally, we test and analyze the proposed model through extensive experiments conducted on IEEE-118 bus system. For every experiment, we present a benchmark analysis against the maintenance models used in current industry practice and power systems literature. Experimental results indicate that the proposed maintenance schedules provide considerable improvements in both cost and reliability.

  • Condition based maintenance for complex distributed systems

    Condition Based Maintenance (CBM) solutions are traditionally challenging to implement for today's complex distributed systems. By their very nature, these systems pose several technical obstacles. The systems to be monitored are distributed, often at remote locations, requiring a data collection network infrastructure that is secure, robust to intermittent connectivity and scalable. Heterogeneous “leading indicator” data is collected from various sources: discrete forms of data such as status, state, mode, system error reporting and inputs from other software systems; parametric data such as environmental sensors and system sensors; and manually collected data such as operator observables and maintenance actions performed. Disparate sources and forms of data also pose a challenge for analysis. Thus, in order to implement a CBM solution for complex distributed systems, it must be based on three core pillars: smart sensors capable of collecting heterogeneous data types, scalable and generically applicable predictive analysis methodologies, and a secure network infrastructure. Mikros is currently deploying a CBM+ system for combat systems on the U.S. Navy's Littoral Combat Ship (LCS). In this CBM+ system application, smart sensors are used to collect heterogeneous data from Navy combat systems. Data is collected in IEEE SIMICA standard format and transferred securely from LCS ships deployed around the world to a central server in the U.S. The Prognostics Framework®, a model-based prognostics reasoning engine, is used to analyze all data to produce prognostic alarms, identify maintenance action needs, report Remaining Useful Life (RUL) of key components, and provide a comprehensive health management capability for the LCS fleet. In summary, heterogeneous data collection made possible through smart sensor technology, model-based prognostics, and a secure network infrastructure provide a flexible and extensible framework to implement CBM for complex distributed systems. Without these core capabilities, CBM falls short of its goals to proactively support maintenance needs, increase system readiness and reliability and reduce overall life-cycle costs of today's complex distributed systems.

  • Multi-sensor monitoring information based decision support method for optimal predictive maintenance policy

    For a class of multi-sensor dynamic systems subject to the latent degradation, a decision support method for condition-based optimal predictive maintenance is proposed in this paper. First, by adopting the distributed filtering and expectation-maximization estimation algorithm, the remaining useful life (RUL) is on-line predicted in accordance with the identified hidden degradation process. Then, a predictive maintenance policy is introduced based on the prediction results. Furthermore, the optimal predictive maintenance time is given by minimizing the maintenance cost. Our main results are verified by a practical case study of the milling machine experiment.



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