A Novel Integrated Machine &amp; Business Intelligence Framework for Sensor  Data Analysis

Kalyani, Sheetal; Sowjanya, A. Mary; Rao, K. Venkat

doi:10.32604/jiot.2021.013163

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…Alpha-investing, on the other hand, requires previous knowledge of the original feature set and never assesses the redundancy among the selected features over time. Sunkara et al [22] presented an integrated framework for IoT Systems that is suitable to engineering and industrial systems, with an emphasis on business and IoT intelligence integration. The current article expands on parts of integrated analysis by addressing the interdependence of sensor variables.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Feature engineering is used to extract few features from the available large number of sensors. An ensemble algorithm with various regression and neural network models including the machine and business intelligence are deployed [22]. This generates quick alarms and plans the operations and maintenance of any complex system automatically.…”

Section: Exploratory Data Analysismentioning

confidence: 99%

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Novel Response Relation Method for Sensor Data Analysis of Complex Engineering Systems

Sunkara¹,

K²,

Sowjanya³

2021

IJSSE

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Technology of Internet of Things (IoT) offers extensive applications for industrial productivity and safety improvement. Advanced miniature sensors are available for monitoring multiple process parameters in a complex industrial or an engineering system. An industrial plant's overall operational status is captured using a network of sensors and stored on a cloud storage platform, where it is evaluated using the machine learning algorithms to produce valuable insights. Finding the correlation among these sensor variables is essential before feeding the same to machine learning algorithms. The present study proposes a novel approach to choose a few critical sensors out of numerous sensors based on the Response Relationship methodology. The Response Relationship method enables the system to be fully autonomous and helps find the interrelation among variables. The Response Relationship among variables is quantified and used for calculating the Remaining Useful Life of a complex engineering system. The proposed methodology is also applied to binary and multi-class classification to demonstrate the efficiency of the Response Relationship method. The results obtained are compared with standard methods of prediction and classification in terms of suitable metrics.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Exploratory Data Analysismentioning

confidence: 99%

Novel Response Relation Method for Sensor Data Analysis of Complex Engineering Systems

Sunkara¹,

K²,

Sowjanya³

2021

IJSSE

View full text Add to dashboard Cite

show abstract

“…In the current scenario, both methods are inadequate to eradicate faults. Therefore, an intelligent predictive maintenance strategy is needed as proposed by Kalyani et al [3] to coordinate the scheduling tasks of maintenance based on failure diagnosis, RUL estimation, and fault prognosis.…”

Section: Introductionmentioning

confidence: 99%

A TimeImageNet Sequence Learning for Remaining Useful Life Estimation of Turbofan Engine in Aircraft Systems

Kalyani¹,

Rao²,

Sowjanya³

2021

Structural Durability &Amp; Health Monitoring

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A Novel Integrated Machine & Business Intelligence Framework for Sensor Data Analysis

Cited by 2 publications

References 7 publications

Novel Response Relation Method for Sensor Data Analysis of Complex Engineering Systems

Novel Response Relation Method for Sensor Data Analysis of Complex Engineering Systems

A TimeImageNet Sequence Learning for Remaining Useful Life Estimation of Turbofan Engine in Aircraft Systems

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