1/10/2024 0 Comments Osmos sabotage![]() To address this challenge, this work proposes a split-learning-based framework (SplitPred) that enables FL clients to maximise available resources within their local network without compromising the benefits of a FL approach (i.e., privacy and shared learning). ![]() FL requires a set of clients that participate in the model training process, however, orchestration, device heterogeneity and scalability can hinder the speed and accuracy in the context of collaborative predictive maintenance. Despite the benefits, FL has some challenges that need to be overcome to make it fully compatible for asset management or more specifically predictive maintenance applications. Federated Learning (FL) has been explored to address these challenges and has been demonstrated to provide a mechanism that allows highly distributed data to be mined in a privacy-preserving manner and offering new opportunities for a collaborative approach to asset management. The availability of data is advantageous for asset management, however, attempts to maximise the value of this data often fall short due to additional constraints, such as privacy concerns and data stored in distributed silos that is difficult to access and share. The proliferation of Industry 4.0 has made modern industrial assets a rich source of data that can be leveraged to optimise operations, ensure efficiency, and minimise maintenance costs. We have evaluated the approach using the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset to predict engines’ RUL Experimental results demonstrate the advantage of FedSVM and FedLSTM in terms of model accuracy, model convergence time, and network usage resources. A global model at the cloud level has also been generated based on these algorithms. This study describes distributed ML for PM applications and proposes two federated algorithms: Federated support vector machine (FedSVM) with memory for anomaly detection and federated long-short term memory (FedLSTM) for remaining useful life (RUL) estimation that enables factories at the fog level to maximize their PM models’ accuracy without compromising their privacy. However, FL has challenges in confronting with asset management in the industry, especially in the PM applications, which need to be considered in order to be fully compatible with these applications. Federated learning (FL) has been proposed a mechanism that provides the ability to create a model from distributed data in edge, fog, and cloud layers without violating privacy and offers new opportunities for a collaborative approach to PM applications. To address this issue, efforts have been made to use edge computing in PM applications., reducing data transmission costs and increasing processing speed. Transmitting massive amounts of data between edge and cloud is costly, increases latency, and causes privacy concerns. ![]() These machines, assumed as edge devices, transmit their data readings to the cloud for processing and modeling. Large amounts of data are collected from machines, processed, and analyzed by different machine learning (ML) algorithms to achieve effective PM. ![]() Industry 4.0 lets the industry build compact, precise, and connected assets and also has made modern industrial assets a massive source of data that can be used in process optimization, defining product quality, and predictive maintenance (PM).
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |