Deep transfer learning for failure prediction across failure types

Li, Zhe; Kristoffersen, Eivind; Li, Jingyue

doi:10.1016/j.cie.2022.108521

Cited by 12 publications

(3 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…High-velocity values usually indicate imbalance or misalignment in the detected system. The work in [21] used rotational speed on a deep transfer learning approach for upcoming failure prediction on a rotor kit. Furthermore, demodulation measurements can be vital for early bearing failure indication.…”

Section: System Definitionmentioning

confidence: 99%

Predictive maintenance framework for assessing health state of centrifugal pumps

Mallioris,

Diamantis,

Bialas

et al. 2024

IJ-AI

View full text Add to dashboard Cite

Combined with advances in sensing technologies and big data analytics, critical information can be extracted from continuous production processes for predicting the health state of equipment and safeguarding upcoming failures. This research presents a methodology for applying predictive maintenance (PdM) solutions and showcases a PdM application for health state prediction and condition monitoring, increasing the safety and productivity of centrifugal pumps for a sustainable and resilient PdM ecosystem. Measurements depicting the healthy and maintenance-prone stages of two centrifugal pumps were collected on the university campus. The dataset consists of 5,118 records and includes both running and standstill values. Additionally, Spearman statistical analysis was conducted to measure the correlation of collected measurements with the predicted output of machine conditions and select the most appropriate features for model optimization. Several machine learning (ML) algorithms, namely random forest (RF), Naïve Bayes, support vector machines (SVM), and <a name="_Hlk137498016"></a>extreme gradient boosting (XGBoost) were analyzed and evaluated during the data mining process. The results indicated the effectiveness and efficiency of XGBoost for the health state prediction of centrifugal pumps. The contribution of this research is to propose an effective framework collectong multistage health data for PdM applications and showcase its effectiveness in a real-world use case.

show abstract

Section: System Definitionmentioning

confidence: 99%

Predictive maintenance framework for assessing health state of centrifugal pumps

Mallioris,

Diamantis,

Bialas

et al. 2024

IJ-AI

View full text Add to dashboard Cite

show abstract

“…Transfer learning and domain adaptation techniques enable researchers to leverage pre-trained models and adapt them to new domains with limited data. These approaches have been used in various applications, such as image classification, object detection, and speech recognition [71]. With the increasing use of black-box models, there is a growing need for explainability and interpretability of model predictions.…”

Section: Advanced Machine Learningmentioning

confidence: 99%

Parametric and Nonparametric Machine Learning Techniques for Increasing Power System Reliability: A Review

Imam,

Musilek,

Reformat

2024

Information

View full text Add to dashboard Cite

Due to aging infrastructure, technical issues, increased demand, and environmental developments, the reliability of power systems is of paramount importance. Utility companies aim to provide uninterrupted and efficient power supply to their customers. To achieve this, they focus on implementing techniques and methods to minimize downtime in power networks and reduce maintenance costs. In addition to traditional statistical methods, modern technologies such as machine learning have become increasingly common for enhancing system reliability and customer satisfaction. The primary objective of this study is to review parametric and nonparametric machine learning techniques and their applications in relation to maintenance-related aspects of power distribution system assets, including (1) distribution lines, (2) transformers, and (3) insulators. Compared to other reviews, this study offers a unique perspective on machine learning algorithms and their predictive capabilities in relation to the critical components of power distribution systems.

show abstract

“…The core of transfer learning lies in the reuse of existing knowledge [31], which can originate from models trained on similar tasks or from tasks in different domains but with transferable characteristics. Especially when facing the challenge of data scarcity, transfer learning shows its unique advantages [32]. By migrating knowledge from existing models, effective learning with fewer data on the target task for application purposes can be achieved.…”

mentioning

confidence: 99%

Prediction of Training Cost and Difficulty for Aircraft-Type Transition Based on Similarity Assessment

Cao,

Zhang,

Feng

2024

Aerospace

View full text Add to dashboard Cite

As aviation technology advances, numerous new aircraft enter the market. These not only offer airlines technological and fuel efficiency advantages but also present the challenge of how to conduct pilots’ aircraft-type transition training efficiently and economically. To address this issue, this study designed a methodology to quantitatively assess the similarity in panel display control design and standard operating procedures (SOPs) between aircraft types. Then, by combining the results of a questionnaire survey on A320, A330, B737, and B777 transition training and training cost data, it was verified quantitatively that inter-aircraft similarity has a positive impact on reducing the difficulty and cost of transition training. Taking the similarity in aircraft types as a feature, the KNN algorithm was used to successfully construct a difficulty prediction model for the training program of aircraft-type transition training. To overcome the limitation of insufficient training cost data volume, this study adopts the transfer learning method to construct a prediction model of the transition training cost, and the final significant prediction accuracy proves the effectiveness of the method. The research in this paper not only provides strong data support for the resource planning and cost management of airlines’ aircraft-type transition training but also provides new research perspectives and methodological guidance for the field of aviation training.

show abstract

Deep transfer learning for failure prediction across failure types

Cited by 12 publications

References 32 publications

Predictive maintenance framework for assessing health state of centrifugal pumps

Predictive maintenance framework for assessing health state of centrifugal pumps

Parametric and Nonparametric Machine Learning Techniques for Increasing Power System Reliability: A Review

Prediction of Training Cost and Difficulty for Aircraft-Type Transition Based on Similarity Assessment

Contact Info

Product

Resources

About