Machinery health prognostics: A systematic review from data acquisition to RUL prediction

https://escholarship.org/content/qt5ns8r3fs/qt5ns8r3fs.pdf?t=pihyfp

Remaining useful life estimation in prognostics using deep convolution neural networks

Battery technology is the bottleneck of the electric vehicles (EVs). It is important, both in theory and practical application, to do research on the modeling and state estimation of batteries, which is essential to optimizing energy management, extending the life cycle, reducing cost, and safeguarding the safe application of batteries in EVs. However, the batteries, with strong time-variables and nonlinear characteristics, are further influenced by such random factors such as driving loads, operational conditions, in the application of EVs. The real-time, accurate estimation of their state is challenging. The classification of the estimation methodologies for estimating state-of-charge (SoC) of battery focusing with the estimation method/algorithm, advantages, drawbacks, and estimation error are systematically and separately discussed. Especially for the battery packs existing of the inevitable inconsistency in cell capacity, resistance and voltage, the advanced characterizing monomer selection, and bias correction-based method has been described and discussed. The review also presents the key feedback factors that are indispensable for accurate estimation of battery SoC, it will be helpful for ensuring the SoC estimation accuracy. It will be very helpful for choosing an appropriate method to develop a reliable and safe battery management system and energy management strategy of the EVs. Finally, the paper also highlights a number of key factors and challenges, and presents the possible recommendations for the development of next generation of smart SoC estimation and battery management systems for electric vehicles and battery energy storage system.

Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles

Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels.

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Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review

Buildings are among the largest consumers of electricity in the US. A significant portion of this energy use in buildings can be attributed to HVAC systems used to maintain comfort for occupants. In most cases these building HVAC systems run on fixed schedules and do not employ any fine grained control based on detailed occupancy information. In this paper we present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices. Our presence sensor is low-cost, wireless, and incrementally deployable within existing buildings. Using a pilot deployment of our system across ten offices over a two week period we identify significant opportunities for energy savings due to periods of vacancy. Our energy measurements show that our presence node has an estimated battery lifetime of over five years, while detecting occupancy accurately. Furthermore, using a building simulation framework and the occupancy information from our testbed, we show potential energy savings from 10% to 15% using our system.

[ACM Press the 2nd ACM Workshop - Zurich, Switzerland (2010.11.02-2010.11.02)] Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building - BuildSys \'10 - Occupancy-driven energy management for smart building automation

Maximum releasable capacity and internal resistance are often used as the health indicators (HIs) of a lithium-ion battery for degradation modeling and estimation of remaining useful life (RUL). However, the maximum releasable capacity is usually difficult to estimate in online applications due to complex operating conditions in the field. Moreover, measuring the internal resistance is too expensive to be implemented on-line. In this paper, an HI extraction and optimization framework requiring only the operating parameters of lithium-ion batteries is proposed for battery degradation modeling and RUL estimation. The framework carries out raw HI extraction, transformation, correlation analysis, and verification and evaluation to achieve HI enhancement. In particular, the Box–Cox transformation is adopted to improve the correlation between the extracted HI and the battery’s actual degradation state. To estimate the battery’s RUL using the enhanced HI, an optimized relevance vector-machine algorithm is utilized, which can be performed in a flexible and agile way. Experimental studies using two different industrial testing data sets illustrate the high efficiency and adaptability of the proposed framework in lithium-ion battery degradation modeling and RUL estimation.

A Health Indicator Extraction and Optimization Framework for Lithium-Ion Battery Degradation Modeling and Prognostics

Lithium-ion battery remaining useful life estimation with an optimized Relevance Vector Machine algorithm with incremental learning

An approach to estimate the remaining useful life (RUL) by Echo State Network (ESN) is presented, which is a new paradigm in recurrent neural network (RNN). ESN randomly establishes a large sparse reservoir to replace the hidden layer of RNN, which overcomes the shortcomings of complicated computing, difficulties in determining the network topology of traditional RNN. An ESN sub-models strategy composed by classified ESN models matching to the varied training data set by retraining and classification is explored to estimate the RUL of turbofan engine system. The experimental results with the turbofan engine data of NASA Ames Prognostics Data Repository show that the proposed method can achieve better RUL estimation precision compared with the approaches of classical ESN and ESN trained by Kalman Filter and potential prospective in application.

A modified echo state network based remaining useful life estimation approach

When localizing the position of an unknown node for wireless sensor networks, the received signal strength indicator (RSSI) value is usually considered to fit a fixed attenuation model with a corresponding communication distance. However, due to some negative factors, the relationship is not valid in the actual localization environment, which leads to a considerable localization error. Therefore, we present a method for improved RSSI-based localization through uncertain data mapping. Starting from an advanced RSSI measurement, the distributions of the RSSI data tuples are determined and expressed in terms of interval data. Then, a data tuple pattern matching strategy is applied to the RSSI data vector during the localization procedure. Experimental results in three representative wireless environments show the feasibility and effectiveness of the proposed approach.

RSSI-Based Localization Through Uncertain Data Mapping for Wireless Sensor Networks

With the wide applications of the unmanned aerial vehicle (UAV), operating safety becomes a critical issue. Thus, fault detection (FD) has been focused, which can realize fault alarm and schedule maintenance in time. Since the accurate physical model of UAV is usually difficult to obtain and flight data with random noise has both spatial and temporal correlation, a huge challenge is posed to FD. In this article, a data-driven multivariate regression approach based on long short-term memory with residual filtering (LSTM-RF) is proposed to fulfill UAV flight data FD and recovery. First, an LSTM network is designed as a regression model, which can extract the spatial–temporal features from the flight data and obtain an estimation of the monitored parameter. Second, a filter is utilized to smooth the residuals between real flight data and estimated values, which mitigates the effect of random noise and dramatically improves the detection performance. Finally, FD is achieved by comparing the smoothed residual with a statistical threshold. Then, fault recovery is fulfilled by replacing fault data with the estimated value. To validate the effectiveness of the proposed method, experiments are conducted based on simulation data and real flight data. The experimental results demonstrate that the proposed method has good performance in FD and recovery of UAV flight data.

Xiyuan Peng

Papers

A Health Indicator Extraction and Optimization Framework for Lithium-Ion Battery Degradation Modeling and Prognostics

Lithium-ion battery remaining useful life estimation with an optimized Relevance Vector Machine algorithm with incremental learning

A modified echo state network based remaining useful life estimation approach

RSSI-Based Localization Through Uncertain Data Mapping for Wireless Sensor Networks

Multivariate Regression-Based Fault Detection and Recovery of UAV Flight Data