Collaborative prognostics in Social Asset Networks

With the spread of Internet of Things (IoT) technologies, assets have acquired communication, processing and sensing capabilities. In response, the fi eld of Asset Management has moved from fleet-wide failure models to individualised asset prognostics. Individualised models are seldom truly distributed, and often fail to capitalise the processing power of the asset fleet. This leads to hardly scalable machine learning centralised models that often must nd a compromise between accuracy and computational power. In order to overcome this, we present a novel theoretical approach to collaborative prognostics within the Social Internet of Things. We introduce the concept of Social Asset Networks, de ned as networks of cooperating assets with sensing, communicating and computing capabilities. In the proposed approach, the information obtained from the medium by means of sensors is synthesised into a Health Indicator, which determines the state of the asset. The Health Indicator of each asset evolves according to an equation determined by a triplet of parameters. Assets are given the form of the equation but they ignore their parametric values. To obtain these values, assets use the equation in order to perform a non-linear least squares t of their Health Indicator data. Using these estimated parameters, they are interconnected to a subset of collaborating assets by means of a similarity metric. We show how by simply interchanging their estimates, networked assets are able to precisely determine their Health Indicator dynamics and reduce maintenance costs. This is done in real time, with no centralised library, and without the need for extensive historical data. We compare Social Asset Networks with the typical self-learning and fleet-wide approaches, and show that Social Asset Networks have a faster convergence and lower cost. This study serves as a conceptual proof for the potential of collaborative prognostics for solving maintenance problems, and can be used to justify the implementation of such a system in a real industrial fleet.EU H202

Distributed Collaborative Prognostics

Managing large fleets of machines in a cost-effective way is becoming more important as corporations own increasingly large amounts of assets. The steady improvement in cost and reliability of sensors, processors and communication devices has helped the spread of a new paradigm: the Internet of Things. This paradigm allows for real-time monitoring of countless physical objects, obtaining data that can be fed to machine learning algorithms to predict their future state and take managerial decisions. Despite rapid technological change, industries have been slow to react, and it has been only recently that many have transitioned towards a new business model: servitisation. Servitisation is based on selling the services that assets provide, instead of the assets themselves. Although more companies are adopting this business model, there is a lack of solutions aimed to maximise its economic value. This thesis presents one such solution capable of predicting failures in real time, thus reducing a crucial cost contribution to asset ownership: unexpected failures. This new approach, Distributed Collaborative Prognostics, consists of providing each machine with its own particular agent, that enables it to communicate with other similar machines in order to improve its failure predictions. This thesis implements Distributed Collaborative Prognostics in three different scenarios: (i) using a multi-agent simulation framework, (ii) using synthetic data from a well-established prognostics data set, and (iii) using real data from a fleet of industrial gas turbines. Each of these scenarios is used to study different elements of the prognostics problem. Multi-agent simulations allow for the calculation of the cost of predictive maintenance coupled with Distributed Collaborative Prognostics, and for the estimation of the cost of agent failures in different architectures. Synthetic data is used as a test bench and to study assets operating in dynamic situations. Real industrial data from the Siemens industrial gas turbine fleet serves to test the applicability of the tool in a real scenario. This thesis concludes that Distributed Collaborative Prognostics is the adequate solution for large and heterogeneous fleets of assets operating dynamically. Its cost effectiveness depends on the value of the assets; in general, highly-valued assets are more conducive to Distributed Collaborative Prognostics, as the savings from improved failure predictions compensate the cost of enabling them with Internet of Things technologies.This PhD Thesis has been supported by a “la Caixa" Fellowship (ID 100010434), with code LCF/BQ/EU17/11590049

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

An Industrial Multi Agent System for real-time distributed collaborative prognostics

Despite increasing interest, real-time prognostics (failure prediction) is still not widespread in industry due to the di fficulties of existing systems to adapt to the dynamic and heterogeneous properties of real asset fleets. In order to address this, we present an Industrial Multi Agent System for real-time distributed collaborative prognostics. Our system fufil ls all six core properties of Advanced Multi Agent Systems: Distribution, Flexibility, Adaptability, Scalability, Leanness, and Resilience. Experimental examples of each are provided for the case of prognostics using the C-MAPPS engine degradation data set, and data from a fleet of industrial gas turbines. Prognostics are performed using the Weibull Time To Event - Recurrent Neural Network algorithm. Collaboration is achieved by sharing information between agents in the system. We conclude that distributed collaborative prognostics is especially pertinent for systems with presence of sensor faults, limited computing capabilities or significant fleet heterogeneity

Multi-agent system architectures for collaborative prognostics

This paper provides a methodology to assess the optimal Multi-Agent architecture for collaborative prognostics in modern fleets of assets. The use of Multi- Agent Systems has been shown to improve the ability to predict equipment failures by enabling machines with communication and collaborative learning capabilities. Di fferent architectures have been postulated for industrial Multi-Agent Systems in general. A rigorous analysis of the implications of their implementation for collaborative prognostics is essential to guide industrial deployment. In this paper, we investigate the cost and reliability implications of using di fferent Multi-Agent Systems architectures for collaborative failure prediction and maintenance optimization in large fleets of industrial assets. Results show that purely distributed architectures are optimal for high-value assets, while hierarchical architectures optimize communication costs for low-value assets. This enables asset managers to design and implement Multi-Agent systems for predictive maintenance that signi ficantly decrease the whole-life cost of their assets.The project that has generated these results has been supported by a la Caixa Fellowship (ID 100010434), with code LCF/BQ/EU17/11590049. This research was partly supported by Siemens Industrial Turbomachinery UK. This research was also partly supported by the Next Generation Converged Digital Infrastructure project (EP/R004935/1) funded by the Engineering and Physical Sciences Research Council and BT. The server used to perform the experiments in this paper was funded by the Centre for Digital Built Britain

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

Secure and communications-efficient collaborative prognosis

Collaborative prognosis is a technique that is used to enable assets to improve their ability to predict failures by learning from the failures of similar other assets. This is typically made possible by enabling the assets to communicate with each other. The key enabler of current collaborative prognosis techniques is that they require assets to share their sensor data and failure information between each other, which might be a major constraint due to commercial sensitivities, especially when the assets belong to different companies. This paper uses Federated Learning to address this issue, and examines whether this technique will enable collaborative prognosis while ensuring sensitive operational data is not shared between organisational boundaries. An example implementation is demonstrated for prognosis of a simulated turbofan fleet, where Federated Averaging algorithm is used as an alternative for the data exchange step. Its performance is compared with conventional collaborative prognosis that involves failure data exchange. The results confirm that Federated Averaging retains the performance of conventional collaborative prognosis, while eliminating the exchange of failure data within assets. This removes a critical hinderance in industrial adoption of collaborative prognosis, thus enhancing the potential of predictive maintenance

A Multi Agent System architecture to implement Collaborative Learning for social industrial assets

The `Industrial Internet of Things' aims to connect industrial assets with one another and subsequently bene t from the data that is generated, and shared, among these assets. In recent years, the extensive instrumentation of machines and the advancements in Information Communication Technologies are re-shaping the role of assets in our industrial systems. An emerging paradigm here is the concept of `social assets': assets that collaborate with each other in order to improve system performance. Cyber-Physical Systems (CPS) are formed by embedding the assets with computing capabilities and linking them with their cyber models. These are known as the `Digital Twins' of the assets, and form the backbone of social assets. Collaboration among assets, by allowing them to share and analyse data from other assets can make embedded computing algorithms more accurate, robust and reliable. This paper proposes a Multi Agent System (MAS) architecture for collaborative learning, and presents the fi ndings of an implementation of this architecture for a prognostics problem. Collaboration among assets is performed by calculating inter-asset similarity during operating condition to identify `friends' and sharing operational data within these clusters of friends. The architecture described in this paper also presents a generic model for the Digital Twins of assets. Prognostics is demonstrated for the C-MAPSS turbofan engine degradation simulated data-set (Saxena and Goebel (2008))

Empirical convergence analysis of federated averaging for failure prognosis

Data driven prognosis involves machine learning algorithms to learn from previous failures and generate its prediction model. However, often a single asset does not fail so frequently to have enough training data in the form of historical failures. This problem can be addressed by learning from failures across a cluster of similar other assets, but often working in different environments. The algorithm therefore must learn from a distributed dataset which might be heterogenous but with underlying similarities. Federated Learning is an emerging technique that has recently also been proposed as a fitting solution for prognosis of industrial assets. However, even the most commonly used Federated Learning algorithms lack theoretical convergence guarantees, and therefore their convergence must be analysed empirically. This paper empirically analyses the convergence of the Federated Averaging (FedAvg) algorithm for a fleet of simulated turbofan engines. Results demonstrate that while FedAvg is applicable for prognosis, it cannot acknowledge the differences in asset failure mechanisms. As a result, the prognosis framework needs to be modified such that similar failures are clustered together before FedAvg can be implemented.This research was funded by the EPSRC and BT Prosperity Part- nership project: Next Generation Converged Digital Infrastructure, grant number EP/R004935/