A Study on Comparison of Classification Algorithms for Pump Failure Prediction

The reliability of pumps can be compromised by faults, impacting their functionality. Detecting these faults is crucial, and many studies have utilized motor current signals for this purpose. However, as pumps are rotational equipped, vibrations also play a vital role in fault identification. Rising pump failures have led to increased maintenance costs and unavailability, emphasizing the need for cost-effective and dependable machinery operation. This study addresses the imperative challenge of defect classification through the lens of predictive modeling. With a problem statement centered on achieving accurate and efficient identification of defects, this study’s objective is to evaluate the performance of five distinct algorithms: Fine Decision Tree, Medium Decision Tree, Bagged Trees (Ensemble), RUS-Boosted Trees, and Boosted Trees. Leveraging a comprehensive dataset, the study meticulously trained and tested each model, analyzing training accuracy, test accuracy, and Area Under the Curve (AUC) metrics. The results showcase the supremacy of the Fine Decision Tree (91.2% training accuracy, 74% test accuracy, AUC 0.80), the robustness of the Ensemble approach (Bagged Trees with 94.9% training accuracy, 99.9% test accuracy, and AUC 1.00), and the competitiveness of Boosted Trees (89.4% training accuracy, 72.2% test accuracy, AUC 0.79) in defect classification. Notably, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and k-Nearest Neighbors (KNN) exhibited comparatively lower performance. Our study contributes valuable insights into the efficacy of these algorithms, guiding practitioners toward optimal model selection for defect classification scenarios. This research lays a foundation for enhanced decision-making in quality control and predictive maintenance, fostering advancements in the realm of defect prediction and classification

Application of response surface methodology (RSM) to optimize COD and ammoniacal nitrogen removal from leachate using moringa and zeolite mixtures

This paper reported the optimization of COD and NH3-N reduction from a stabilised leachate by zeolite (ZE) and moringa oleifera leaf powder (MP) mixture using response surface methodology (RSM) and central composite design (CCD). Quadratic polynomial equations were obtained for the removal process. An initial experiment was conducted to establish the optimum mixed ratio between ZE:MP and resulted in a ratio of 24:16. Independent variables investigated in the subsequent optimization experiments include pH ,dosage and contact time. The results revealed that the optimal reduction of COD and NH3-N from landfill leachate was considerable at pH 5.9 , optimal time of 113 minutes and 100gL-1 of adsorbent dosage with desirability value of 0.917. The upper limits for the actual versus predicted reduction were 70.14 against 69.13%  and 86.94 against 86.55 % respectively for COD and NH3-N which defined that the experimental values were relatively close to the predicted values. The study also revealed that ZE:MP mixture has a very high potential for the remediation of COD and NH3-N from a stabilized leachate

Optimization of Batch Conditions for COD and Ammonia Nitrogen Removal Using cockle shells Through Response Surface Methodology

The optimal conditions for the reduction of COD and NH3-N using cockle shells (CS) from a stabilised landfill effluent were analyzed. The influence of two variables (adsorbent dosage and pH) were analysed through the application of response surface methodology (RSM) and central composite design (CCD). Quadratic models were developed for the removals of COD and NH3-N parameters. The optimum conditions for removal of 65.6% and 53.6% for COD and NH3-N respectively was achieved at pH 6.34, adsorbent dosage of 20.21 g having 0.888 desirability value. The model F-value obtained for NH3-N removal Prob. > F value of 0.0001 with F-value of 104.21 was obtained. Similarly the Prob. > F value of < 0.0001 for COD with F-value of 82.74 was obtained, these P-values confirmed the significance of the model. The predicted response versus the experimental response depicted that the experimental data were relatively close to the predicted data. Thus, the generated models significantly enclosed the correlation between the process variables and the response.   GMT Detect languageAfrikaansAlbanianArabicArmenianAzerbaijaniBasqueBelarusianBengaliBosnianBulgarianCatalanCebuanoChichewaChinese (Simplified)Chinese (Traditional)CroatianCzechDanishDutchEnglishEsperantoEstonianFilipinoFinnishFrenchGalicianGeorgianGermanGreekGujaratiHaitian CreoleHausaHebrewHindiHmongHungarianIcelandicIgboIndonesianIrishItalianJapaneseJavaneseKannadaKazakhKhmerKoreanLaoLatinLatvianLithuanianMacedonianMalagasyMalayMalayalamMalteseMaoriMarathiMongolianMyanmar (Burmese)NepaliNorwegianPersianPolishPortuguesePunjabiRomanianRussianSerbianSesothoSinhalaSlovakSlovenianSomaliSpanishSundaneseSwahiliSwedishTajikTamilTeluguThaiTurkishUkrainianUrduUzbekVietnameseWelshYiddishYorubaZulu AfrikaansAlbanianArabicArmenianAzerbaijaniBasqueBelarusianBengaliBosnianBulgarianCatalanCebuanoChichewaChinese (Simplified)Chinese (Traditional)CroatianCzechDanishDutchEnglishEsperantoEstonianFilipinoFinnishFrenchGalicianGeorgianGermanGreekGujaratiHaitian CreoleHausaHebrewHindiHmongHungarianIcelandicIgboIndonesianIrishItalianJapaneseJavaneseKannadaKazakhKhmerKoreanLaoLatinLatvianLithuanianMacedonianMalagasyMalayMalayalamMalteseMaoriMarathiMongolianMyanmar (Burmese)NepaliNorwegianPersianPolishPortuguesePunjabiRomanianRussianSerbianSesothoSinhalaSlovakSlovenianSomaliSpanishSundaneseSwahiliSwedishTajikTamilTeluguThaiTurkishUkrainianUrduUzbekVietnameseWelshYiddishYorubaZulu         Text-to-speech function is limited to 200 characters  Options : History : Feedback : DonateClos

Prognostic Health Management of Pumps Using Artificial Intelligence in the Oil and Gas Sector: A Review

A system’s operational life cycle now includes an integrated health management and diagnostic strategy due to improvements in the current technology. It is evident that the life cycle may be used to identify abnormalities, analyze failures, and forecast future conditions based on current data. Data models can be trained using machine learning and statistical ideas, employing condition data and on-site feedback. Once data models are trained, the data-processing logic can be integrated into onboard controllers, allowing for real-time health evaluation and analysis. Interestingly, the oil and gas industries may encounter numerous obstacles and hurdles as a result of the integration, highlighting the need for creative solutions to the perplexing problem. The potential benefits in terms of challenges involving feature extraction and data classification, machine learning has received significant research attention recently. The application and utility in pump system health management should be investigated to explore the extend it can be used to increase overall system resilience or identify potential financial advantages for maintenance, repair, and overhaul activities. This is seen as an evolving research area, with a variety of application domains. This article present a critical analysis of machine learning’s most current advances in the field of artificial intelligence-based system health management, specifically in terms of pump applications in the oil and gas industries. To further understand its potential, various algorithms and related theories are examined. Based on the examined studies, machine learning shows potential for prognostics and defect diagnosis. There are, few drawbacks that is seen to be preventing its widespread adoption which prompt for further improvement. The article discussed possible solutions to the identified drawbacks and future opportunities presented. This study further elaborates on the commonly available commercial machine learning (ML) tools used for pump fault prognostics and diagnostics with an emphasis on the type of data utilized. Findings from the literature review shows that the neural network (NN) is the most prevalent algorithm employed in studies, followed by the Bayesian network (BN), support vector machine (SVM), and hybrid models. While the need for selecting appropriate training algorithms is seen to be significant. Interestingly, no specific method or algorithm exists for a given problem instead the solution relies on the type of data and the algorithm’s or method’s aptitude for resolving the provided errors. Among the various research studies on pump fault diagnosis and prognosis, the most frequently discussed problem is a bearing fault, with a percentage of 46%, followed by cavitation. The studies rank seal damage as the third most prevalent flaw. Leakage and obstruction are the least studied defects in research. The main data types used in machine learning techniques for diagnosing pump faults are vibration and flow, which might not be sufficient to identify the condition of pumps and their characteristics. The various datasets have been derived from expert opinion, real-world observations, laboratory tests, and computer simulations. Field data have frequently been used to create experimental datasets and simulated data. In comparison to the algorithmic approach, the data approach has not received significant research attention

Prognostic Health Management of Pumps Using Artificial Intelligence in the Oil and Gas Sector: A Review

A system’s operational life cycle now includes an integrated health management and diagnostic strategy due to improvements in the current technology. It is evident that the life cycle may be used to identify abnormalities, analyze failures, and forecast future conditions based on current data. Data models can be trained using machine learning and statistical ideas, employing condition data and on-site feedback. Once data models are trained, the data-processing logic can be integrated into onboard controllers, allowing for real-time health evaluation and analysis. Interestingly, the oil and gas industries may encounter numerous obstacles and hurdles as a result of the integration, highlighting the need for creative solutions to the perplexing problem. The potential benefits in terms of challenges involving feature extraction and data classification, machine learning has received significant research attention recently. The application and utility in pump system health management should be investigated to explore the extend it can be used to increase overall system resilience or identify potential financial advantages for maintenance, repair, and overhaul activities. This is seen as an evolving research area, with a variety of application domains. This article present a critical analysis of machine learning’s most current advances in the field of artificial intelligence-based system health management, specifically in terms of pump applications in the oil and gas industries. To further understand its potential, various algorithms and related theories are examined. Based on the examined studies, machine learning shows potential for prognostics and defect diagnosis. There are, few drawbacks that is seen to be preventing its widespread adoption which prompt for further improvement. The article discussed possible solutions to the identified drawbacks and future opportunities presented. This study further elaborates on the commonly available commercial machine learning (ML) tools used for pump fault prognostics and diagnostics with an emphasis on the type of data utilized. Findings from the literature review shows that the neural network (NN) is the most prevalent algorithm employed in studies, followed by the Bayesian network (BN), support vector machine (SVM), and hybrid models. While the need for selecting appropriate training algorithms is seen to be significant. Interestingly, no specific method or algorithm exists for a given problem instead the solution relies on the type of data and the algorithm’s or method’s aptitude for resolving the provided errors. Among the various research studies on pump fault diagnosis and prognosis, the most frequently discussed problem is a bearing fault, with a percentage of 46%, followed by cavitation. The studies rank seal damage as the third most prevalent flaw. Leakage and obstruction are the least studied defects in research. The main data types used in machine learning techniques for diagnosing pump faults are vibration and flow, which might not be sufficient to identify the condition of pumps and their characteristics. The various datasets have been derived from expert opinion, real-world observations, laboratory tests, and computer simulations. Field data have frequently been used to create experimental datasets and simulated data. In comparison to the algorithmic approach, the data approach has not received significant research attention

Application of response surface methodology (RSM) to optimize COD and ammoniacal nitrogen removal from leachate using moringa and zeolite mixtures

This paper reported the optimization of COD and NH3-N reduction from a stabilised leachate by zeolite (ZE) and moringa oleifera leaf powder (MP) mixture using response surface methodology (RSM) and central composite design (CCD). Quadratic polynomial equations were obtained for the removal process. An initial experiment was conducted to establish the optimum mixed ratio between ZE:MP and resulted in a ratio of 24:16. Independent variables investigated in the subsequent optimization experiments include pH ,dosage and contact time. The results revealed that the optimal reduction of COD and NH3-N from landfill leachate was considerable at pH 5.9 , optimal time of 113 minutes and 100gL-1 of adsorbent dosage with desirability value of 0.917. The upper limits for the actual versus predicted reduction were 70.14 against 69.13%  and 86.94 against 86.55 % respectively for COD and NH3-N which defined that the experimental values were relatively close to the predicted values. The study also revealed that ZE:MP mixture has a very high potential for the remediation of COD and NH3-N from a stabilized leachate

Optimization of batch conditions for COD and ammonia nitrogen removal using cockle shells through response surface methodology

The optimal conditions for the reduction of COD and NH3-N using cockle shells (CS) from a stabilised landfill effluent were analyzed. The influence of two variables (adsorbent dosage and pH) were analysed through the application of response surface methodology (RSM) and central composite design (CCD). Quadratic models were developed for the removals of COD and NH3-N parameters. The optimum conditions for removal of 65.6% and 53.6% for COD and NH3-N respectively was achieved at pH 6.34, adsorbent dosage of 20.21 g having 0.888 desirability value. The model F-value obtained for NH3-N removal Prob. > F value of 0.0001 with F-value of 104.21 was obtained. Similarly the Prob. > F value of < 0.0001 for COD with F-value of 82.74 was obtained, these P-values confirmed the significance of the model. The predicted response versus the experimental response depicted that the experimental data were relatively close to the predicted data. Thus, the generated models significantly enclosed the correlation between the process variables and the response