Fault diagnosis of various rotating equipment using machine learning approaches – A review

2020 ◽  
pp. 095440892097197
Author(s):  
S Manikandan ◽  
K Duraivelu
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Learning Strategies ◽  
Early Identification ◽  
Deep Neural Network ◽  
Fundamental Aspect ◽  
Learning Approaches ◽  
Multiple Components ◽  
Rotating Equipment

Fault diagnosis of various rotating equipment plays a significant role in industries as it guarantees safety, reliability and prevents breakdown and loss of any source of energy. Early identification is a fundamental aspect for diagnosing the faults which saves both time and costs and in fact it avoids perilous conditions. Investigations are being carried out for intelligent fault diagnosis using machine learning approaches. This article analyses various machine learning approaches used for fault diagnosis of rotating equipment. In addition to this, a detailed study of different machine learning strategies which are incorporated on various rotating equipment in the context of fault diagnosis is also carried out. Mainly, the benefits and advance patterns of deep neural network which are applied to multiple components for fault diagnosis are inspected in this study. Finally, different algorithms are proposed to propagate the quality of fault diagnosis and the conceivable research ideas of applying machine learning approaches on various rotating equipment are condensed in this article.

scholarly journals Learning approaches and studies of effect of environmental factors

2009 ◽  
Vol 41 (1) ◽  
pp. 25-44 ◽  
Author(s):  
Snezana Mirkov
Keyword(s):  
Learning Strategies ◽  
Approaches To Learning ◽  
Learning Approaches ◽  
Cultural Research ◽  
Cross Cultural Research ◽  
Research Findings ◽  
Different Cultures ◽  
3P Model

There is a presentation of 3P model of learning (Presage-Process-Product), which comprises learning approaches placed in a wider context of the set of variables related to personality, environment, process and outcomes of learning. Three approaches to learning - surface, deep and achievement-oriented - consist of motives and the corresponding learning strategies. There is a discussion of the findings and implications of a great deal of research using the instruments based on this model. We analyzed research findings about the effect of instruction on learning approaches acquired by pupils, and especially students. It is shown how based on learning approach employed by pupils it is possible to draw conclusions about the quality of instruction. Testing the instruments on various samples indicates that the model is applicable in different cultures. Cross-cultural research opened up the problem of relation between memorising and understanding. Further research is necessary, both empirical and theoretical, that is, development of conceptualization of these constructs, and especially their role in education. Perspectives for further research also open up in the direction of studying various factors connected with personality and their relations with learning approaches. The role of learning approaches of teachers in developing the learning approaches of pupils is yet to be examined.

scholarly journals A machine learning-based clustering approach to diagnose multi-component degradation of aircraft fuel systems

2021 ◽  
Author(s):  
Haochen Liu ◽  
Yifan Zhao ◽  
Anna Zaporowska ◽  
Zakwan Skaf
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Engineering Systems ◽  
Diagnosis And Prognosis ◽  
System A ◽  
Multiple Components ◽  
Fault Severity ◽  
Aircraft Fuel ◽  
Clustering Approach ◽  
Fault Diagnosis And Prognosis

AbstractAccurate fault diagnosis and prognosis can significantly reduce maintenance costs, increase the safety and availability of engineering systems that have become increasingly complex. It has been observed that very limited researches have been reported on fault diagnosis where multi-component degradation are presented. This is essentially a challenging Complex Systems problem where features multiple components interacting simultaneously and nonlinearly with each other and its environment on multiple levels. Even the degradation of a single component can lead to a misidentification of the fault severity level. This paper introduces a new test rig to simulate the multi-component degradation of the aircraft fuel system. A machine learning-based data analytical approach based on the classification of clustering features from both time and frequency domains is proposed. The scope of this framework is the identification of the location and severity of not only the system fault but also the multi-component degradation. The results illustrate that (a) the fault can be detected with accuracy > 99%; (b) the severity of fault can be identified with an accuracy of almost 100%; (c) the degradation level can be successfully identified with the R-square value > 0.9.

Overview of Machine Learning Approaches

2017 ◽  
pp. 19-33
Keyword(s):  
Machine Learning ◽  
Software Quality ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Case Based Reasoning ◽  
Learning Approaches ◽  
Software Module ◽  
Learning Techniques ◽  
Case Based

This chapter enlists and presents an overview of various machine learning approaches. It also explains the machine learning techniques used in the area of software engineering domain especially case-based reasoning method. Case-based reasoning is used to predict software quality of the system by examining a software module and predicting whether it is faulty or non-faulty. In this chapter an attempt has been made to propose a model with the help of previous data which is used for prediction. In this chapter, how machine learning technique such as case-based reasoning has been used for error estimation or fault prediction. Apart from case-based reasoning, some other types of learning methods have been discussed in detail.

scholarly journals Machine Learning in Chronic Pain Research: A Scoping Review

2021 ◽  
Vol 11 (7) ◽  
pp. 3205
Author(s):  
Marit Dagny Kristine Jenssen ◽  
Per Atle Bakkevoll ◽  
Phuong Dinh Ngo ◽  
Andrius Budrionis ◽  
Asbjørn Johansen Fagerlund ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Pain ◽  
Literature Search ◽  
Self Management ◽  
Learning Approaches ◽  
Societal Costs ◽  
Pain Research ◽  
Computing Machinery ◽  
High Prevalence

Given the high prevalence and associated cost of chronic pain, it has a significant impact on individuals and society. Improvements in the treatment and management of chronic pain may increase patients’ quality of life and reduce societal costs. In this paper, we evaluate state-of-the-art machine learning approaches in chronic pain research. A literature search was conducted using the PubMed, IEEE Xplore, and the Association of Computing Machinery (ACM) Digital Library databases. Relevant studies were identified by screening titles and abstracts for keywords related to chronic pain and machine learning, followed by analysing full texts. Two hundred and eighty-seven publications were identified in the literature search. In total, fifty-three papers on chronic pain research and machine learning were reviewed. The review showed that while many studies have emphasised machine learning-based classification for the diagnosis of chronic pain, far less attention has been paid to the treatment and management of chronic pain. More research is needed on machine learning approaches to the treatment, rehabilitation, and self-management of chronic pain. As with other chronic conditions, patient involvement and self-management are crucial. In order to achieve this, patients with chronic pain need digital tools that can help them make decisions about their own treatment and care.

scholarly journals Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 546
Author(s):  
Omer Mujahid ◽  
Ivan Contreras ◽  
Josep Vehi
Keyword(s):  
Machine Learning ◽  
Diabetic Patient ◽  
Life Quality ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Diabetic Patients ◽  
Learning Approaches ◽  
Prediction Horizon ◽  
Learning Techniques

(1) Background: the use of machine learning techniques for the purpose of anticipating hypoglycemia has increased considerably in the past few years. Hypoglycemia is the drop in blood glucose below critical levels in diabetic patients. This may cause loss of cognitive ability, seizures, and in extreme cases, death. In almost half of all the severe cases, hypoglycemia arrives unannounced and is essentially asymptomatic. The inability of a diabetic patient to anticipate and intervene the occurrence of a hypoglycemic event often results in crisis. Hence, the prediction of hypoglycemia is a vital step in improving the life quality of a diabetic patient. The objective of this paper is to review work performed in the domain of hypoglycemia prediction by using machine learning and also to explore the latest trends and challenges that the researchers face in this area; (2) Methods: literature obtained from PubMed and Google Scholar was reviewed. Manuscripts from the last five years were searched for this purpose. A total of 903 papers were initially selected of which 57 papers were eventually shortlisted for detailed review; (3) Results: a thorough dissection of the shortlisted manuscripts provided an interesting split between the works based on two categories: hypoglycemia prediction and hypoglycemia detection. The entire review was carried out keeping this categorical distinction in perspective while providing a thorough overview of the machine learning approaches used to anticipate hypoglycemia, the type of training data, and the prediction horizon.

scholarly journals A Systematic Review of Predicting Elections Based on Social Media Data: Research Challenges and Future Directions

2021 ◽  
Vol 9 (10) ◽  
pp. 1410-1440
Author(s):  
T Heena Fayaz
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Social Networks ◽  
Social Media ◽  
Future Research ◽  
Learning Approaches ◽  
Electoral Campaigns ◽  
Regression Methods ◽  
New Research

Abstract: The way politicians communicate with the electorateand run electoral campaigns was reshaped by the emergence and popularization of contemporary social media (SM), such as Facebook, Twitter, and Instagram social networks (SN). Due to inherent capabilities of SM, such as the large amount of available data accessed in real time, a new research subject has emerged, focusing on using SM data to predict election outcomes. Despite many studies conducted in the last decade, results are very controversial, and many times challenged. In this context, this work aims to investigate and summarize how research on predicting elections based on SM data has evolved since its beginning, to outline the state of both the art and the practice,and to identify research opportunities within this field. In termsof method, we performed a systematic literature review analyzingthe quantity and quality of publications, the electoral context of studies, the main approaches to and characteristics of the successful studies, as well as their main strengths and challenges, and compared our results with previous reviews. We identified and analyzed 83 relevant studies, and the challenges were identified in many areas such as process, sampling, modeling, performance evaluation and scientific rigor. Main findings include the low success of the most-used approach, namely volume and sentiment analysis on Twitter, and the better results with new approaches, such as regression methods trained with traditional polls. Finally, a vision of future research on integrating advances on process definitions, modeling, and evaluation is also discussed, pointing out, among others, the need for better investigating the application of state-of-art machine learning approaches. Index Terms: Elections, Social Media, Social Networks, Machine Learning, Systematic Review

Learning and Explaining the Impact of Enterprises’ Organizational Quality on their Economic Results

2012 ◽  
pp. 228-248 ◽  
Author(s):  
Marko Pregeljc ◽  
Erik Štrumbelj ◽  
Miran Mihelcic ◽  
Igor Kononenko
Keyword(s):  
Machine Learning ◽  
Learning Approaches ◽  
Data Set ◽  
Economic Interpretation ◽  
Organizational Quality ◽  
Social Units ◽  
The Impact ◽  
Insight Into ◽  
General Explanation

The authors employed traditional and novel machine learning to improve insight into the connections between the quality of an organization of enterprises as a type of formal social units and the results of enterprises’ performance in this chapter. The analyzed data set contains 72 Slovenian enterprises’ economic results across four years and indicators of their organizational quality. The authors hypothesize that a causal relationship exists between the latter and the former. In the first part of a two-part process, they use several classification algorithms to study these relationships and to evaluate how accurately they predict the target economic results. However, the most successful models were often very complex and difficult to interpret, especially for non-technical users. Therefore, in the second part, the authors take advantage of a novel general explanation method that can be used to explain the influence of individual features on the model’s prediction. Results show that traditional machine-learning approaches are successful at modeling the dependency relationship. Furthermore, the explanation of the influence of the input features on the predicted economic results provides insights that have a meaningful economic interpretation.

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