Joseph Vivek, Naveen Venkatesh S., Tapan K. Mahanta, Sugumaran V., M. Amarnath, Sangharatna M. Ramteke and Max Marian
This study aims to explore the integration of machine learning (ML) in tribology to optimize lubrication interval decisions, aiming to enhance equipment lifespan and operational…
Abstract
Purpose
This study aims to explore the integration of machine learning (ML) in tribology to optimize lubrication interval decisions, aiming to enhance equipment lifespan and operational efficiency through wear image analysis.
Design/methodology/approach
Using a data set of scanning electron microscopy images from an internal combustion engine, the authors used AlexNet as the feature extraction algorithm and the J48 decision tree algorithm for feature selection and compared 15 ML classifiers from the lazy-, Bayes and tree-based families.
Findings
From the analyzed ML classifiers, instance-based k-nearest neighbor emerged as the optimal algorithm with a 95% classification accuracy against testing data. This surpassed individually trained convolutional neural networks’ (CNNs) and closely approached ensemble deep learning (DL) techniques’ accuracy.
Originality/value
The proposed approach simplifies the process, enhances efficiency and improves interpretability compared to more complex CNNs and ensemble DL techniques.
Details
Keywords
Kiran Vernekar, Hemantha Kumar and Gangadharan K.V.
Bearings and gears are major components in any rotatory machines and, thus, gained interest for condition monitoring. The failure of such critical components may cause an increase…
Abstract
Purpose
Bearings and gears are major components in any rotatory machines and, thus, gained interest for condition monitoring. The failure of such critical components may cause an increase in down time and maintenance cost. Condition monitoring using the machine learning approach is a conceivable solution for the problem raised during the operation of the machinery system. The paper aims to discuss these issues.
Design/methodology/approach
This paper aims engine gearbox fault diagnosis based on a decision tree and artificial neural network algorithm.
Findings
The experimental result (classification accuracy 85.55 percent) validates that the proposed approach is an effective method for engine gearbox fault diagnosis.
Originality/value
This paper attempts to diagnose the faults in engine gearbox based on the machine learning approach with the combination of statistical features of vibration signals, decision tree and multi-layer perceptron neural network techniques.
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Keywords
P.K. Joshi, M. Munsi and A. Joshi
Global climate can be defined as the average of all the regional trends of weather over a long time period (National Science Foundation [NSF], 2009). The researchers all over the…
Abstract
Global climate can be defined as the average of all the regional trends of weather over a long time period (National Science Foundation [NSF], 2009). The researchers all over the world have concluded that the Earth's climate is changing as a whole. There are basically two factors that have impacts on the climate, the natural (climatic and environmental variability) and the anthropogenic (infrastructure development and land use land cover change). The causes of past changes are believed to be related to changes in ocean currents, solar activity, volcanic eruptions, and other natural factors (ISDR, 2008). But in recent times, human activities have accelerated this rate of climate change (IPCC, 2007; Sperling & Szekely, 2005; ISDR, 2008). As the developmental activities increase, the amount of emission of greenhouse gasses and aerosols increases, which, in turn, leads to global warming and snow melting, thus increasing the sea level and the frequency and intensity of cyclones, floods, droughts, and many other disasters (IPCC, 2001).
This paper aims to design an AI-based drone that can facilitate the complicated and time-intensive control process for detecting healthy and defective solar panels. Today, the use…
Abstract
Purpose
This paper aims to design an AI-based drone that can facilitate the complicated and time-intensive control process for detecting healthy and defective solar panels. Today, the use of solar panels is becoming widespread, and control problems are increasing. Physical control of the solar panels is critical in obtaining electrical power. Controlling solar panel power plants and rooftop panel applications installed in large areas can be difficult and time-consuming. Therefore, this paper designs a system that aims to panel detection.
Design/methodology/approach
This paper designed a low-cost AI-based unmanned aerial vehicle to reduce the difficulty of the control process. Convolutional neural network based AI models were developed to classify solar panels as damaged, dusty and normal. Two approaches to the solar panel detection model were adopted: Approach 1 and Approach 2.
Findings
The training was conducted with YOLOv5, YOLOv6 and YOLOv8 models in Approach 1. The best F1 score was 81% at 150 epochs with YOLOv5m. In total, 87% and 89% of the best F1 score and mAP values were obtained with the YOLOv5s model at 100 epochs in Approach 2 as a proposed method. The best models at Approaches 1 and 2 were used with a developed AI-based drone in the real-time test application.
Originality/value
The AI-based low-cost solar panel detection drone was developed with an original data set of 1,100 images. A detailed comparative analysis of YOLOv5, YOLOv6 and YOLOv8 models regarding performance metrics was realized. Gaussian, salt-pepper noise addition and wavelet transform noise removal preprocessing techniques were applied to the created data set under the proposed method. The proposed method demonstrated expressive and remarkable performance in panel detection applications.
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Keywords
Abla Chaouni Benabdellah, Kamar Zekhnini, Surajit Bag, Shivam Gupta and Ana Beatriz Lopes de Sousa Jabbour
This study aims to propose a collaborative knowledge-based ontological research model for designing a collaborative product development process (PDP) while considering different…
Abstract
Purpose
This study aims to propose a collaborative knowledge-based ontological research model for designing a collaborative product development process (PDP) while considering different design for X techniques.
Design/methodology/approach
This study follows a thematic literature analysis to identify the key design concepts needed to assess environmental, service, safety, manufacture and assembly, supply chain and quality concerns in developing a collaborative PDP.
Findings
The proposed model provides a guide for methodology, engineering and ontology evaluation metrics (verification, assessment and validation). The findings benefit both practitioners and managers because they address the key knowledge taxonomy needed to assist them in storing information, promoting teamwork and making decisions in a collaborative PDP while incorporating various design for X approaches and product life cycles.
Originality/value
This study introduces a novel knowledge-based collaborative ontological research model, which is specifically designed to tackle the challenges of developing collaborative products in the contemporary landscape. The model presents a significant and valuable contribution to the field by introducing an ontological approach for acquiring, representing and leveraging knowledge in a computer-interpretable format to support the design of collaborative products. In addition, it provides a comprehensive guide for evaluating the effectiveness and efficacy of the ontology developed.
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Keywords
Aisong Qin, Qin Hu, Qinghua Zhang, Yunrong Lv and Guoxi Sun
Rotating machineries are widely used in manufacturing, petroleum, chemical, aircraft, and other industries. To accurately identify the operating conditions of such rotating…
Abstract
Purpose
Rotating machineries are widely used in manufacturing, petroleum, chemical, aircraft, and other industries. To accurately identify the operating conditions of such rotating machineries, this paper aims to propose a fault diagnosis method based on sensitive dimensionless parameters and particle swarm optimization (PSO)–support vector machine (SVM) for reducing the unexpected downtime and economic losses.
Design/methodology/approach
A relatively new hybrid intelligent fault classification approach is proposed by integrating multiple dimensionless parameters, the Fisher criterion and PSO–SVM. In terms of data pre-processing, a method based on wavelet packet decomposition (WPD), empirical mode decomposition (EMD) and dimensionless parameters is proposed for the extraction of the vibration signal features. The Fisher criterion is applied to reduce the redundant dimensionless parameters and search for the sensitive dimensionless parameters. Then, PSO is adapted to optimize the penalty parameter and kernel parameter for SVM. Finally, the sensitive dimensionless parameters are classified with the optimized model.
Findings
As two different time–frequency analysis methods, a method based on a combination of WPD and EMD used to extract multiple dimensionless parameters is presented. More vital diagnosis information can be obtained from the vibration signals than by only using a single time–frequency analysis method. Besides, a fault classification approach combining the sensitive dimensionless parameters and PSO-SVM classifier is proposed. The comparative experiment results show that the proposed method has a high classification accuracy and efficiency.
Originality/value
To the best of the authors’ knowledge, very few efforts have been performed for fault classification using multiple dimensionless parameters. In this paper, eighty dimensionless parameters have been studied intensively, which provides a new strategy in fault diagnosis field.
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Martin Aruldoss, Miranda Lakshmi Travis and V. Prasanna Venkatesan
Business intelligence (BI) has been applied in various domains to take better decisions and it provides different level of information to its stakeholders according to the…
Abstract
Purpose
Business intelligence (BI) has been applied in various domains to take better decisions and it provides different level of information to its stakeholders according to the information needs. The purpose of this paper is to present a literature review on recent works in BI. The two principal aims in this survey are to identify areas lacking in recent research, thereby offering potential opportunities for investigation.
Design/methodology/approach
To simplify the study on BI literature, it is segregated into seven categories according to the usage. Each category of work is analyzed using parameters such as purpose, domain, problem identified, solution applied, benefit and outcome.
Findings
The BI contribution in various domains, ongoing research in BI, the convergence of BI domains, problems and solutions, results of congregated domains, core problems and key solutions. It also outlines BI and its components composition, widely applied BI solutions such as algorithm-based, architecture-based and model-based solutions. Finally, it discusses BI implementation issues and outlines the security and privacy policies adopted in BI environment.
Research limitations/implications
In this survey BI has been discussed in theoretical perspective whereas practical contribution has been given less attention.
Originality/value
A comprehensive survey on BI which identifies areas lacking in recent research and providing potential opportunities for investigation.
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Yudith Cardinale, Maria Alejandra Cornejo-Lupa, Alexander Pinto-De la Gala and Regina Ticona-Herrera
This study aims to the OQuaRE quality model to the developed methodology.
Abstract
Purpose
This study aims to the OQuaRE quality model to the developed methodology.
Design/methodology/approach
Ontologies are formal, well-defined and flexible representations of knowledge related to a specific domain. They provide the base to develop efficient and interoperable solutions. Hence, a proliferation of ontologies in many domains is unleashed. Then, it is necessary to define how to compare such ontologies to decide which one is the most suitable for the specific needs of users/developers. As the emerging development of ontologies, several studies have proposed criteria to evaluate them.
Findings
In a previous study, the authors propose a methodological process to qualitatively and quantitatively compare ontologies at Lexical, Structural and Domain Knowledge levels, considering correctness and quality perspectives. As the evaluation methods of the proposal are based on a golden-standard, it can be customized to compare ontologies in any domain.
Practical implications
To show the suitability of the proposal, the authors apply the methodological approach to conduct comparative studies of ontologies in two different domains, one in the robotic area, in particular for the simultaneous localization and mapping (SLAM) problem; and the other one, in the cultural heritage domain. With these cases of study, the authors demonstrate that with this methodological comparative process, we are able to identify the strengths and weaknesses of ontologies, as well as the gaps still needed to fill in the target domains.
Originality/value
Using these metrics and the quality model from OQuaRE, the authors are incorporating a standard of software engineering at the quality validation into the Semantic Web.
Details
Keywords
Joerg Leukel and Vijayan Sugumaran
Process models specific to the supply chain domain are an important tool for the analysis of interorganizational interfaces and requirements of information technology (IT) systems…
Abstract
Purpose
Process models specific to the supply chain domain are an important tool for the analysis of interorganizational interfaces and requirements of information technology (IT) systems supporting supply chain decision-making. The purpose of this study is to examine the effectiveness of supply chain process models for novice analysts in conveying domain semantics compared to alternative textual representations.
Design/methodology/approach
A laboratory experiment with graduate students as proxies for novice analysts was conducted. Participants were randomly assigned to either the diagram group, which worked with “thread diagrams” created from the modeling grammar “Supply Chain Operation Reference (SCOR) model”, or the text group, which worked with semantically equivalent textual representations. Domain understanding was measured using cognitively demanding information acquisition for two different domains.
Findings
Diagram users were more accurate in identifying product-related information and organizing this information in a graph compared to those using the textual representation. The authors found considerable improvements in domain understanding, and using the diagrams was perceived as easy as using the texts.
Originality/value
The study's findings are unique in providing empirical evidence for supply chain process models being an effective representation for novice analysts. Such evidence is lacking in prior research because of the evaluation methods used, which are limited to scenario, case study and informed argument. This study adds the diagram user's perspective to that literature and provides a rigorous empirical evaluation by contrasting diagrammatic and textual representations.