A random forest based machine learning approach for mild steel defect diagnosis

Patel, Sanjay V.; Jokhakar, Veena N.

doi:10.1109/iccic.2016.7919549

Cited by 25 publications

(8 citation statements)

References 14 publications

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“…In the mild steel coil manufacturing plants, large amount of data is generated with the help of many sensors deployed to measure different parameters which can be used for defect diagnosis of the coils produced [42]. Patel and Jokhakar (2016) proposed defect cause analysis model to be applied in steel industry [43]. The result showed that random forest can achieve accuracy of 95% compared to other algorithm.…”

Section: Quality Improvement Based On Data Miningmentioning

confidence: 73%

An Open Source-Based Real-Time Data Processing Architecture Framework for Manufacturing Sustainability

Syafrudin

Fitriyani

et al. 2017

Sustainability

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Currently, the manufacturing industry is experiencing a data-driven revolution. There are multiple processes in the manufacturing industry and will eventually generate a large amount of data. Collecting, analyzing and storing a large amount of data are one of key elements of the smart manufacturing industry. To ensure that all processes within the manufacturing industry are functioning smoothly, the big data processing is needed. Thus, in this study an open source-based real-time data processing (OSRDP) architecture framework was proposed. OSRDP architecture framework consists of several open sources technologies, including Apache Kafka, Apache Storm and NoSQL MongoDB that are effective and cost efficient for real-time data processing. Several experiments and impact analysis for manufacturing sustainability are provided. The results showed that the proposed system is capable of processing a massive sensor data efficiently when the number of sensors data and devices increases. In addition, the data mining based on Random Forest is presented to predict the quality of products given the sensor data as the input. The Random Forest successfully classifies the defect and non-defect products, and generates high accuracy compared to other data mining algorithms. This study is expected to support the management in their decision-making for product quality inspection and support manufacturing sustainability.

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Section: Quality Improvement Based On Data Miningmentioning

confidence: 73%

An Open Source-Based Real-Time Data Processing Architecture Framework for Manufacturing Sustainability

Syafrudin

Fitriyani

et al. 2017

Sustainability

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“…We evaluated the performance of the following regression models using the sequence vector input scheme: linear, 95 lasso, 96 ridge, 97 K -neighbors, 98 random forest, 99 and MLP. 99 Lasso and ridge regressions are linear regression schemes that use L 1 and L 2 regularization, respectively, which results in lasso preferring to shrink some weights to zero. Evaluating these three models gives insight into how effectively each input feature produces accurate output.…”

Section: Resultsmentioning

confidence: 99%

Predicting aggregate morphology of sequence-defined macromolecules with recurrent neural networks

et al. 2022

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“…Random Forest, so-called ensemble decision trees, has been used in this work as a classifier algorithm for it gives better predictive results and also operates building multiple decision trees, providing faults detection with higher reliability and accuracy as compared with DT especially when the data are originally expanded. 55,56 Furtherly, RF is used to reduce the difference between the actual and predicted values like variance, bias, and noise which is not functionally included in RF.…”

Section: Methodsmentioning

confidence: 99%

An integrated machine learning: Utility theory framework for real-time predictive maintenance in pumping systems

Khorsheed

Beyca

2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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Bearings are the most widely used mechanical parts in rotating machinery under high load and high rotational speeds. Operating continuously under such harsh conditions, wear and failure are imminent. Developing defects give rise to even-higher vibration and temperature levels. In general, mechanical defects in a machine cause high vibration levels. Therefore, bearing fault identification and early detection enables the maintenance team to repair the problem before it triggers catastrophic failure in the bearing. Machine downtime is thus avoided or minimized. This paper explores the use of Machine Learning (ML) integrated with decision-making techniques to predict possible bearing failures and improve the overall manufacturing operations by applying the correct maintenance actions at the right time. The accuracy of the Predictive Maintenance (PdM) module has been tested on real industrial production datasets. The paper proposes an effective PdM methodology using different ML algorithms to detect failures before they happen and reduce pump downtime. The performance of the tested ML algorithms is based on five performance indicators: accuracy, precision, F-score, recall, and an area under curve (AUC). Experimental results revealed that all tested ML algorithms are successful and effective. Furthermore, decision making with utility theory has been employed to exploit the probability of failures and thus help to perform the appropriate maintenance interventions. This provides a logical framework for decision-makers to identify the optimum action with the maximum expected benefit. As a case study, the model is applied on forwarding pumping stations belonging to the Sewerage Treatment Company (STC), one of the largest sewage stations in Qatar.

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A random forest based machine learning approach for mild steel defect diagnosis

Cited by 25 publications

References 14 publications

An Open Source-Based Real-Time Data Processing Architecture Framework for Manufacturing Sustainability

An Open Source-Based Real-Time Data Processing Architecture Framework for Manufacturing Sustainability

Predicting aggregate morphology of sequence-defined macromolecules with recurrent neural networks

An integrated machine learning: Utility theory framework for real-time predictive maintenance in pumping systems

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