Fast capacity prediction of lithium-ion batteries using aging mechanism-informed bidirectional long short-term memory network

The multi-stage degradation process of bearings significantly affects the predicted performance of rotating machinery and equipment in long-term operation. However, the inherent correlations between degradation stages, and domain-invariant degradation features of multistage that affect the remaining useful life (RUL) are mostly ignored, leading to RUL prognostics deterioration under cross-working conditions. Therefore, a novel deep transfer learning-based hierarchical adaptive RUL prediction approach is applied to overcome this problem. Firstly, a novel multistage degradation (MD) division method is proposed with a combination of maximum mean discrepancy and statistical process analysis to accurately obtain the varied health indicators (HIs) with MD without setting any threshold. Then, the obtained MD features are fed into the residual network to automatically recognize the HIs and MD in response to multi-variability trend changes. Finally, a novel hierarchical adaptive RUL model based on a deep adaptive-transformer is proposed to transfer the learned domain-invariant degradation features of multistage from one to various working conditions. Meanwhile, hierarchical adaptive tuning (HAT) reduces the RUL model loss to guarantee a more accurate prediction of each degradation stage. To validate the proposed approach, extensive experiments were conducted in seven cross-domain cases on the XJTU-SY dataset to predict RUL. Comparison results indicate that the proposed approach is more accurate than other conventional methods because it considers the RUL of bearing at each stage. 

Deep transfer learning-based hierarchical adaptive remaining useful life prediction of bearings considering the correlation of multistage degradation

Data-driven industrial health prognostics require rich training data to develop accurate and reliable predictive models. However, stringent data privacy laws and the abundance of edge industrial data necessitate decentralized data utilization. Thus, the industrial health prognostics field is well suited to significantly benefit from federated learning (FL), a decentralized and privacy-preserving learning technique. However, FL-based health prognostics tasks have hardly been investigated due to the complexities of meaningfully aggregating model parameters trained from heterogeneous data to form a high performing federated model. Specifically, data heterogeneity among edge devices, stemming from dissimilar degradation mechanisms and unequal dataset sizes, poses a critical statistical challenge for developing accurate federated models. We propose a pioneering FL-based health prognostic model with a feature similarity-matched parameter aggregation algorithm to discriminatingly learn from heterogeneous edge data. The algorithm searches across the heterogeneous locally trained models and matches neurons with probabilistically similar feature extraction functions first, before selectively averaging them to form the federated model parameters. As the algorithm only averages similar neurons, as opposed to conventional naive averaging of coordinate-wise neurons, the distinct feature extractors of local models are carried over with less dilution to the resultant federated model. Using both cyclic degradation data of Li-ion batteries and non-cyclic data of turbofan engines, we demonstrate that the proposed method yields accuracy improvements as high as 44.5\% and 39.3\% for state-of-health estimation and remaining useful life estimation, respectively. 

/pdf/a-federated-learning-based-industrial-health-prognostics-for-do897tb6.pdf

A Federated Learning-Based Industrial Health Prognostics for Heterogeneous Edge Devices Using Matched Feature Extraction

Remaining useful life prediction combined dynamic model with transfer learning under insufficient degradation data

In the age of Industry 5.0, prognostics and health management (PHM) is very important for proactive and scheduled maintenance in industrial processes. The target of prognosis is the health state prediction of the system or machine under consideration, hence its Remaining Useful Life RUL. The life of a tool, a part, or a component of the system must be tracked to increase its productivity, reduce human effort and save lives. Data driven prognostics is highly relying on statistical or artificial intelligence AI methods including machine learning (ML) and deep learning (DL) models. AI is a massive enlarging field with encouraging outcomes in prognostics for modelling of data with complex representations and temporal dependencies. A sample of latest research in prognostics especially in industry applications has been collected during this research. About 76% of the collected research papers used data-driven prognostics in their model including 48% applied DL different architectures for prognostic purposes in industrial systems in the last few years. Therefore, this survey concentrate on presenting AI-based data-driven prognostics in industrial systems especially DL-based architectures. The study also puts spot on the main challenges with opportunities of future work in the DL-based PHM applications in the age of Industry 5.0. 

Artificial Intelligence-based data-driven prognostics in Industry: A survey

Deep Transfer Learning Based on Bi-LSTM and Attention for Remaining Useful Life Prediction of Rolling Bearing

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Deep Transfer Learning Based on Bi-LSTM and Attention for Remaining Useful Life Prediction of Rolling Bearing