6 research outputs found
Recurrent Neural Networks for real-time distributed collaborative prognostics
We present the first steps towards real-time distributed collaborative prognostics enabled by an implementation of the Weibull Time To Event - Recurrent Neural Network (WTTE-RNN) algorithm. In our system, assets determine their time to failure (TTF) in real-time according to an asset-specific model that is obtained in collaboration with other similar assets in the asset fleet. The presented approach builds on the emergent field of similarity analysis in asset management, and extends it to distributed collaborative prognostics. We show how through collaboration between assets and distributed prognostics, competitive time to failure estimates can be obtained
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Federated Learning for Collaborative Prognosis
Modern industrial assets generate prodigious condition monitoring data. Various prognosis techniques can use this data to predict the asset’s remaining useful life. But the data in most asset fleets is distributed across multiple assets, bound by the privacy policies of the operators, and often legally protected. Such peculiar characteristics make data-driven prognosis an interesting problem. In this paper, we propose Federated Learning as a solution to the above mentioned challenges. Federated Learning enables the manufacturer to utilise condition monitoring data without moving it away from the corresponding assets. Concretely, we demonstrate Federated Averaging algorithm to train feed-forward, and recurrent neural networks for predicting failures in a simulated turbofan fleet. We also analyse the dependence of prediction quality on the various learning parameters.1. Siemens Industrial Turbomachinery U
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Distributed diagnostics, prognostics and maintenance planning: Realizing industry 4.0
In this paper, a novel distributed yet integrated approach for diagnostics and prognostics is presented. An experimental study is conducted to validate the performance. Results showed that distributed prognostics give better performance in leaser computational time. Also, the proposed approach helps in making the results of the machine learning techniques comprehensible and more accurate. These results will be handy in arriving at predictive maintenance schedule considering the criticality of the system, the dependency of the components, available maintenance resources and confidence level in the results of the prognostic.Royal Academy of Engineering London, UK (IAPP 18-19/31
Recurrent Neural Networks for real-time distributed collaborative prognostics
We present the first steps towards real-time distributed collaborative prognostics enabled by an implementation of the Weibull Time To Event - Recurrent Neural Network (WTTE-RNN) algorithm. In our system, assets determine their time to failure (TTF) in real-time according to an asset-specific model that is obtained in collaboration with other similar assets in the asset fleet. The presented approach builds on the emergent field of similarity analysis in asset management, and extends it to distributed collaborative prognostics. We show how through collaboration between assets and distributed prognostics, competitive time to failure estimates can be obtained
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Integrated Workload Allocation and Condition-based Maintenance Threshold Optimisation
Effective asset management is considered key to reducing total costs of asset ownership while enhancing machine availability, guaranteeing security, and increasing productivity. Amongst all the activities involved in asset management, maintenance has been one of the major focus areas of academic research due to its potential in helping manufacturers to generate the most value from their assets. The emergence of condition-based maintenance (CBM) in which decisions are made based on the real-time condition of assets, has opened up new possibilities in developing more comprehensive approaches to improve the performance of production systems. For instance, a trend has been observed where attempts are made to couple CBM decisions with those on other production-related factors such as inventory control, spare parts management, and labour routing. The intrinsic link between the degradation behaviour of and the workload allocated to an asset, however, has not been sufficiently studied. Consequently, the potential benefits of intervening in machine degradation, either in the context of a single asset or a fleet of assets, are rarely explored. It is therefore essential that a systematic approach is at hand to improve system performance by exploiting the inter-relationship between production and maintenance.
This thesis is dedicated to developing a dynamic integrated decision-making model to improve the system-level performance of a fleet of parallel assets. The aim of the model is to realise the potential benefits, mainly in the form of lower maintenance costs and reduced penalty costs incurred due to loss of production, by simultaneously optimising workload allocation and the CBM threshold. The decision-making model is implemented using an agent-based system involving two types of agents - 1) machine agents that reside within each individual machine; and 2) a coordinator agent that oversees the entire system. The integrated decision-making model is constituted of two components - 1) a workload-dependent condition-based maintenance optimisation model based on Gamma Process at the asset level through a machine agent; and 2) a workload allocation strategy at the system level implemented by a coordinator agent. Numerical analysis is performed to demonstrate the rationale behind the decision-making process, which is to reach the most desirable balance between maintenance costs and penalty costs incurred by loss of production. The capability of the model to reduce total costs is demonstrated via comparison with traditional strategies such as uniform and random workload allocation. Additionally, the sensitivity analysis conducted has helped to reveal the respective factors that impact the potential reduction in maintenance costs and that in penalty costs, which include the sensitivity of asset degradation to workloads, heterogeneity of assets, penalty cost for a unit of production loss, redundancy of the system, etc.
The model presented in this study not only assists operation and maintenance managers to make decisions on the optimal combination of workload allocation and maintenance plans for assets in a production system, but also provides guidance on whether they should invest in workload control capabilities. Furthermore, the proposed approach allows practitioners to evaluate the long-term impacts of sudden events such as an increase in demand, a decrease in the number of redundant machines, and a change in the cost of maintenance actions