Fleet Prognosis with Physics-informed Recurrent Neural Networks

Services and warranties of large fleets of engineering assets is a very profitable business. The success of companies in that area is often related to predictive maintenance driven by advanced analytics. Therefore, accurate modeling, as a way to understand how the complex interactions between operating conditions and component capability define useful life, is key for services profitability. Unfortunately, building prognosis models for large fleets is a daunting task as factors such as duty cycle variation, harsh environments, inadequate maintenance, and problems with mass production can lead to large discrepancies between designed and observed useful lives. This paper introduces a novel physics-informed neural network approach to prognosis by extending recurrent neural networks to cumulative damage models. We propose a new recurrent neural network cell designed to merge physics-informed and data-driven layers. With that, engineers and scientists have the chance to use physics-informed layers to model parts that are well understood (e.g., fatigue crack growth) and use data-driven layers to model parts that are poorly characterized (e.g., internal loads). A simple numerical experiment is used to present the main features of the proposed physics-informed recurrent neural network for damage accumulation. The test problem consist of predicting fatigue crack length for a synthetic fleet of airplanes subject to different mission mixes. The model is trained using full observation inputs (far-field loads) and very limited observation of outputs (crack length at inspection for only a portion of the fleet). The results demonstrate that our proposed hybrid physics-informed recurrent neural network is able to accurately model fatigue crack growth even when the observed distribution of crack length does not match with the (unobservable) fleet distribution.Comment: Data and codes (including our implementation for both the multi-layer perceptron, the stress intensity and Paris law layers, the cumulative damage cell, as well as python driver scripts) used in this manuscript are publicly available on GitHub at https://github.com/PML-UCF/pinn. The data and code are released under the MIT Licens

Survey on wireless technology trade-offs for the industrial internet of things

Aside from vast deployment cost reduction, Industrial Wireless Sensor and Actuator Networks (IWSAN) introduce a new level of industrial connectivity. Wireless connection of sensors and actuators in industrial environments not only enables wireless monitoring and actuation, it also enables coordination of production stages, connecting mobile robots and autonomous transport vehicles, as well as localization and tracking of assets. All these opportunities already inspired the development of many wireless technologies in an effort to fully enable Industry 4.0. However, different technologies significantly differ in performance and capabilities, none being capable of supporting all industrial use cases. When designing a network solution, one must be aware of the capabilities and the trade-offs that prospective technologies have. This paper evaluates the technologies potentially suitable for IWSAN solutions covering an entire industrial site with limited infrastructure cost and discusses their trade-offs in an effort to provide information for choosing the most suitable technology for the use case of interest. The comparative discussion presented in this paper aims to enable engineers to choose the most suitable wireless technology for their specific IWSAN deployment

Topological structures in the equities market network

We present a new method for articulating scale-dependent topological descriptions of the network structure inherent in many complex systems. The technique is based on "Partition Decoupled Null Models,'' a new class of null models that incorporate the interaction of clustered partitions into a random model and generalize the Gaussian ensemble. As an application we analyze a correlation matrix derived from four years of close prices of equities in the NYSE and NASDAQ. In this example we expose (1) a natural structure composed of two interacting partitions of the market that both agrees with and generalizes standard notions of scale (eg., sector and industry) and (2) structure in the first partition that is a topological manifestation of a well-known pattern of capital flow called "sector rotation.'' Our approach gives rise to a natural form of multiresolution analysis of the underlying time series that naturally decomposes the basic data in terms of the effects of the different scales at which it clusters. The equities market is a prototypical complex system and we expect that our approach will be of use in understanding a broad class of complex systems in which correlation structures are resident.Comment: 17 pages, 4 figures, 3 table

Analysis of DVB-H network coverage with the application of transmit diversity

This paper investigates the effects of the Cyclic Delay Diversity (CDD) transmit diversity scheme on DVB-H networks. Transmit diversity improves reception and Quality of Service (QoS) in areas of poor coverage such as sparsely populated or obscured locations. The technique not only povides robust reception in mobile environments thus improving QoS, but it also reduces network costs in terms of the transmit power, number of infrastructure elements, antenna height and the frequency reuse factor over indoor and outdoor environments. In this paper, the benefit and effectiveness of CDD transmit diversity is tackled through simulation results for comparison in several scenarios of coverage in DVB-H networks. The channel model used in the simulations is based on COST207 and a basic radio planning technique is used to illustrate the main principles developed in this paper. The work reported in this paper was supported by the European Commission IST project—PLUTO (Physical Layer DVB Transmission Optimization)

Measurements, processing functions and laboratory test-bed experiments for evaluating diversity in broadcast network

This paper presents a test-bed development and measurement plan for evaluating transmit diversity and on-channel repeaters in the Digital Video Broadcasting Network. Transmit diversity reduces the complexity and improves the power consumption of the personal receiving devices by enhancing the transmission of signals in NLOS cluttered environments. It is more practical than receive diversity due to the difficulty of locating two receive antennas far enough apart in a small mobile device. The on-channel repeater is to extend the coverage of the DVB-T/H network in areas where services are inaccessible by receiving the DVB-T/H signals off air, amplifying and then retransmitting it on the same frequency as received. Test service scenarios were developed to illustrate the benefits of such technologies so that effectiveness can be researched in a variety of service and terrain scenarios using purpose built test systems.The work presented in this paper was supported by the European Commission IST project PLUTO