Machine learning-based predictive maintenance: A cost-oriented model for implementation

Florian, Eleonora; Sgarbossa, Fabio; Zennaro, Ilenia

doi:10.1016/j.ijpe.2021.108114

Cited by 64 publications

(26 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, in order to meet these targets, it is crucial to ensure the profitability of wind turbines, by improving their performances and reducing their operation and maintenance costs. In this context, many predictive maintenance (PdM) techniques have been developed to predict failures before they happen and to optimize maintenance interventions [4][5][6][7][8]. In [6], a data-driven decision-making strategy that incorporates prognostic and health management is proposed for a wind farm.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, Machine Learning (ML) is seen as a key enabling approach for PdM of wind turbines. Many articles highlighting the use of ML for PdM of wind turbines have already been published [4,5,[9][10][11][12][13][14][15][16][17]. In [4], the authors analyzed 2.8 million sensor data collected from 31 wind turbines and used Random Forest (RF) and Decision Trees (DT) [18] to construct predictive models for wind turbines.…”

Section: Introductionmentioning

confidence: 99%

“…They proved that wind turbines failures can be detected and maintenance needs can be predicted using ML. In [5], a cost-oriented analysis was proposed to define when ML-based PdM is the most suitable maintenance strategy. This study involved investment costs as well as costs incurred from traditional maintenance activities such as repair, replacement, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Though ML community lauds the importance of data preprocessing steps, it appears that most of the papers in the literature proposed a model-centric ML methodology [4,5,9,[12][13][14][15][16][17]19]. This latter focuses on the optimization of ML model architectures and its hyperparameters, in order to improve the performance, whereas data-related aspects are often neglected.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Data-Centric Machine Learning Methodology: Application on Predictive Maintenance of Wind Turbines

2022

View full text Add to dashboard Cite

Nowadays, the energy sector is experiencing a profound transition. Among all renewable energy sources, wind energy is the most developed technology across the world. To ensure the profitability of wind turbines, it is essential to develop predictive maintenance strategies that will optimize energy production while preventing unexpected downtimes. With the huge amount of data collected every day, machine learning is seen as a key enabling approach for predictive maintenance of wind turbines. However, most of the effort is put into the optimization of the model architectures and its parameters, whereas data-related aspects are often neglected. The goal of this paper is to contribute to a better understanding of wind turbines through a data-centric machine learning methodology. In particular, we focus on the optimization of data preprocessing and feature selection steps of the machine learning pipeline. The proposed methodology is used to detect failures affecting five components on a wind farm composed of five turbines. Despite the simplicity of the used machine learning model (a decision tree), the methodology outperformed model-centric approach by improving the prediction of the remaining useful life of the wind farm, making it more reliable and contributing to the global efforts towards tackling climate change.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Data-Centric Machine Learning Methodology: Application on Predictive Maintenance of Wind Turbines

2022

View full text Add to dashboard Cite

show abstract

“…This strategy can avoid unnecessary maintenance plans and ensure the reliability of equipment operation. It has been widely used in recent years [8]. Health prognostics is one of the major tasks in CBM, it can provide important guidance for equipment maintenance.…”

Section: Introductionmentioning

confidence: 99%

Machine remaining life prediction based on multi-layer self-attention and temporal convolution network

Shang

Zhang

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

Convolution neural network (CNN) has been widely used in the field of remaining useful life (RUL) prediction. However, the CNN-based RUL prediction methods have some limitations. The receptive field of CNN is limited and easy to happen gradient vanishing problem when the network is too deep. The contribution differences of different channels and different time steps to RUL prediction are not considered, and only use deep learning features or handcrafted statistical features for prediction. These limitations can lead to inaccurate prediction results. To solve these problems, this paper proposes an RUL prediction method based on multi-layer self-attention (MLSA) and temporal convolution network (TCN). The TCN is used to extract deep learning features. Dilated convolution and residual connection are adopted in TCN structure. Dilated convolution is an efficient way to widen receptive field, and the residual structure can avoid the gradient vanishing problem. Besides, we propose a feature fusion method to fuse deep learning features and statistical features. And the MLSA is designed to adaptively assign feature weights. Finally, the turbofan engine dataset is used to verify the proposed method. Experimental results indicate the effectiveness of the proposed method.

show abstract

Transition paths for condition‐based maintenance‐driven smart services

Akkermans,

Basten,

Zhu

et al. 2024

J of Ops Management

View full text Add to dashboard Cite

This research investigates growth inhibitors for smart services driven by condition‐based maintenance (CBM). Despite the fast rise of Industry 4.0 technologies, such as smart sensoring, internet of things, and machine learning (ML), smart services have failed to keep pace. Combined, these technologies enable CBM to achieve the lean goal of high reliability and low waste for industrial equipment. Equipment located at customers throughout the world can be monitored and maintained by manufacturers and service providers, but so far industry uptake has been slow. The contributions of this study are twofold. First, it uncovers industry settings that impede the use of equipment failure data needed to train ML algorithms to predict failures and use these predictions to trigger maintenance. These empirical settings, drawn from four global machine equipment manufacturers, include either under‐ or over‐maintenance (i.e., either too much or too little periodic maintenance). Second, formal analysis of a system dynamics model based on these empirical settings reveals a sweet spot of industry settings in which such inhibitors are absent. Companies that fall outside this sweet spot need to follow specific transition paths to reach it. This research discusses these paths, from both a research and practice perspective.

show abstract

Machine learning-based predictive maintenance: A cost-oriented model for implementation

Cited by 64 publications

References 38 publications

A Data-Centric Machine Learning Methodology: Application on Predictive Maintenance of Wind Turbines

A Data-Centric Machine Learning Methodology: Application on Predictive Maintenance of Wind Turbines

Machine remaining life prediction based on multi-layer self-attention and temporal convolution network

Transition paths for condition‐based maintenance‐driven smart services

Contact Info

Product

Resources

About