SMART: Integrating Human Safety Risk Assessment with Asset Integrity

Galar, Diego; Sandborn, Peter; Kumar, Uday; Johansson, Carl-Anders

doi:10.1007/978-3-642-39348-8_3

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Galar et al 36,20,37 A more complete, accurate assessment of the condition led to better diagnostics and prognostics. Consequently, safety and reliability were increased, and life-cycle maintenance costs were reduced.…”

Section: /3/5 Integrated Machine-and Human-based Diagnostics and Promentioning

confidence: 99%

“…In a series of three papers, Galar et al 36,20,37 promoted a hybrid, data-driven, and phenomenological approach to fusion in order to aid the condition-based maintenance of railway assets (e.g., locomotives, track components, interchanges). In a Machinery Information Management Open System Alliance (MIMOSA) framework, measurements were collected from electronic sensors.…”

Section: Level 2/3/5 Integrated Machine-and Human-based Diagnostics Amentioning

confidence: 99%

See 1 more Smart Citation

Application of the JDL data fusion process model to hard/soft information fusion in the condition monitoring of aircraft

Bernardo

2014

SPIE Proceedings

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Hard/soft information fusion has been proposed as a way to enhance diagnostic capability for the condition monitoring of machinery. However, there is a limited understanding of where hard/soft information fusion could and should be applied in the condition monitoring of aircraft. Condition-based maintenance refers to the philosophy of performing maintenance when the need arises, based upon indicators of deterioration in the condition of the machinery. The addition of the multisensory capability of human cognition to electronic sensors may create a fuller picture of machinery condition.Since 1988, the Joint Directors of Laboratories (JDL) data fusion process model has served as a framework for information fusion research. Advances are described in the application of hard/soft information fusion in condition monitoring using terms that condition-based maintenance professionals in aviation will recognize. Emerging literature on hard/soft information fusion in condition monitoring is organized into the levels of the JDL data fusion process model. Gaps in the literature are identified, and the author's ongoing research is discussed. Future efforts will focus on building domain-specific frameworks and experimental design, which may provide a foundation for improving flight safety, increasing mission readiness, and reducing the cost of maintenance operations.

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Section: /3/5 Integrated Machine-and Human-based Diagnostics and Promentioning

confidence: 99%

Section: Level 2/3/5 Integrated Machine-and Human-based Diagnostics Amentioning

confidence: 99%

Application of the JDL data fusion process model to hard/soft information fusion in the condition monitoring of aircraft

Bernardo

2014

SPIE Proceedings

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“…It is hard to fulfill the practical requirement of application specific scientific approaches for the industries while performing real time data analysis and condition assessment towards preventive maintenance as the machines are operating at different environmental conditions. Diego Galar et al [1] have cited that single valued thresholds provided by the manufactures are not suitable for fault identification under non-stationary operations, environmental changes and aging. The vibration severity characterized by ISO 10816 has been specified as a static threshold suitable for new machines and said to provide incorrect reference for machines in use.…”

Section: State Of the Artmentioning

confidence: 99%

An IoT Based Cloud Deployment Framework for Effective Classification of Machine Conditions

Dhandapani¹,

Ramachandran²

2021

Internet of Things

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Cloud services are proposed for real-time data acquisition, data classification, data processing and decision making, which are highly interconnected services for effective condition monitoring of electrical machines. The proposed Software as a Service, Storage as a Service and Platform as a Service layers address the challenges of data storage and scalability while making analysis on the cluster of machines in an Industrial Environment. An experimental setup consisting of two DC motors coupled to AC Generator operating at different locations is considered to evolve the proposed model for effective integrated monitoring and decision making. This cloud-based vibration monitoring model provides services for data acquisition from the IoT devices mounted on the shafts of the DC motors, data storage to store the enormous amount of acquired signal data from multiple sensors, data classification of vibration signals for effective statistical analysis to estimate adaptive cluster of thresholds and appropriate decision-making services on demand over the Internet to utilize the reliable service of the machines in a persistent way. The computational engine will do inherent statistical analysis of the vibration signals to estimate the cluster of thresholds adaptive to various operating conditions. The services have been deployed without any limitation in a cloud environment and the industrial applications can share information using the deployed services from anywhere on demand basis. The deployed cloud service for the enhanced statistical classification algorithm eliminates the false identification of failures, which not only increase the availability of machines for intended operations but also reduce the maintenance cost. The resulting threshold values are compared with that of the vibration analysis carried out on the machine beds locally using myRIO for data acquisition in LabVIEW and the proposed model ensures the integrity in appropriate decision making with assured scalability.

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Efficient Methodology for Estimation of Vibration Thresholds for Electrical Machines

Ganga

Ramachandran

2018

Advances in Intelligent Systems and Computing

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SMART: Integrating Human Safety Risk Assessment with Asset Integrity

Cited by 3 publications

References 9 publications

Application of the JDL data fusion process model to hard/soft information fusion in the condition monitoring of aircraft

Application of the JDL data fusion process model to hard/soft information fusion in the condition monitoring of aircraft

An IoT Based Cloud Deployment Framework for Effective Classification of Machine Conditions

Efficient Methodology for Estimation of Vibration Thresholds for Electrical Machines

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