Sensors For Condition-based Maintenance
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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.
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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.
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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 ...
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 ...
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 ...
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 ...
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 ...
Heuristics for Design for Reliability in Electrical and Electronic Products
High Throughput Neural Network based Embedded Streaming Multicore Processors - Tarek Taha: 2016 International Conference on Rebooting Computing
IEEE Magnetics Distinguished Lecture - Alison B. Flatau
2013 IEEE Dennis J. Picard Medal
ISEC 2013 Special Gordon Donaldson Session: Remembering Gordon Donaldson - 7 of 7 - SQUID-based noise thermometers for sub-Kelvin thermometry
Lighting the Way: Optical Sensors in the Life Sciences
A Transformer-Based Inverted Complementary Cross-Coupled VCO with a 193.3dBc/Hz FoM and 13kHz 1/f3 Noise Corner: RFIC Interactive Forum
How to Cope with an Increasing Number of Objectives in Optimization - Xin Yao - WCCI 2016
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Uncovering the Neural Code of Learning Control - Jennie Si - WCCI 2012 invited lecture
Dr. Scott Fish
Materials Challenges for Next-Generation, High-Density Magnetic Recording - Kazuhiro Hono: IEEE Magnetics Distinguished Lecture 2016
EDOC 2010 - Sylvain Halle - Best Paper Presentation
Yuan-ting Zhang AMA EMBS Individualized Health
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Implantable Wireless Medical Devices and Systems
IEEE @ SXSW 2015 - Future of Identity Series Overview
Alexandros Fragkiadakis: Trust-based Scheme Employing Evidence Reasoning for IoT Architectures: WF-IoT 2016
Advanced Capacitive Sensing for Consumer, Industrial, and Automotive Applications - Lecture by Dr. Hans Klein
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.
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.
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.
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.
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.
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.
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.
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 (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.
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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