A New Data Structure for Representation of Relational Databases for Application in the Normalization Process

Jadhav, Ranjana; Dhabe, Priyadarshan; Gandewar, Sadanand; Mirani, Param; Chugwani, Rahul

doi:10.1007/978-981-15-1884-3_29

Cited by 3 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Normalization is a way to standardize and minimize the problems arising from these dispersions. In addition, by not analyzing data considered inconsistent, data processing is also more efficient [36]. This work employs minimummaximum normalization, which uses the minimum and maximum values of each feature to define a common range yield, as is illustrated in Equation (13).…”

Section: Data Normalizationmentioning

confidence: 99%

Diesel Engine Fault Prediction Using Artificial Intelligence Regression Methods

et al. 2023

View full text Add to dashboard Cite

Predictive maintenance has been employed to reduce maintenance costs and production losses and to prevent any failure before it occurs. The framework proposed in this work performs diesel engine prognosis by evaluating the absolute value of the failure severity using random forest (RF) and multilayer perceptron (MLP) neural networks. A database was implemented with 3500 failure scenarios to overcome the problem of inducing destructive failures in diesel engines. Diesel engine failure signals were developed with the zero-dimensional thermodynamic model inside a cylinder coupled with the crankshaft torsional vibration model. Artificial neural networks and random forest regression models were employed for classifying and quantifying failures. The methodology was applied alongside an engine simulator to assess effectiveness and accuracy. The best-fitting performance was obtained with the random forest regressor with an RMSE value of 0.10 ± 0.03%.

show abstract

Section: Data Normalizationmentioning

confidence: 99%