Circuit breaker prognostics using SF6 data

Control decisions within future energy networks may take account of the health and condition of network assets, pushing condition monitoring within the smart grid remit. In order to support maintenance decisions, this paper proposes a circuit breaker prognostic system, which ranks circuit breakers in order of maintenance priority. By monitoring the SF6 density within a breaker, the system not only predicts the number of days to a critical level, but also incorporates uncertainty by giving upper and lower bounds on the prediction. This prognostic model, which performs linear regression, will be described in this paper, along with case studies demonstrating ranking breakers based on maintenance priority and prognosis of a leaking breaker. Providing an asset manager with this type of information could allow improved management of his/her assets, potentially deferring maintenance to a time when an outage is already scheduled

Functional Principal Component Analysis for Extrapolating Multi-stream Longitudinal Data

The advance of modern sensor technologies enables collection of multi-stream longitudinal data where multiple signals from different units are collected in real-time. In this article, we present a non-parametric approach to predict the evolution of multi-stream longitudinal data for an in-service unit through borrowing strength from other historical units. Our approach first decomposes each stream into a linear combination of eigenfunctions and their corresponding functional principal component (FPC) scores. A Gaussian process prior for the FPC scores is then established based on a functional semi-metric that measures similarities between streams of historical units and the in-service unit. Finally, an empirical Bayesian updating strategy is derived to update the established prior using real-time stream data obtained from the in-service unit. Experiments on synthetic and real world data show that the proposed framework outperforms state-of-the-art approaches and can effectively account for heterogeneity as well as achieve high predictive accuracy

The latent state hazard model, with application to wind turbine reliability

We present a new model for reliability analysis that is able to distinguish the latent internal vulnerability state of the equipment from the vulnerability caused by temporary external sources. Consider a wind farm where each turbine is running under the external effects of temperature, wind speed and direction, etc. The turbine might fail because of the external effects of a spike in temperature. If it does not fail during the temperature spike, it could still fail due to internal degradation, and the spike could cause (or be an indication of) this degradation. The ability to identify the underlying latent state can help better understand the effects of external sources and thus lead to more robust decision-making. We present an experimental study using SCADA sensor measurements from wind turbines in Italy.Comment: Published at http://dx.doi.org/10.1214/15-AOAS859 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Remaining useful life prediction of the ball screw system based on weighted Mahalanobis distance and an exponential model

The ball screw system is one of the crucial components of machine tools and predicting its remaining useful life (RUL) can enhance the reliability and safety of the entire machine tool and reduce maintenance costs. Although quite a few techniques have been developed for the fault diagnosis of the ball screw system, forecasting the RUL of the ball screw system is a remaining challenge. To make up for this deficiency, we present a model-based method to predict the RUL of the ball screw system, which consists of two parts: health indicator (HI) construction and RUL prediction. First, we develop a novel HI, weighted Mahalanobis distance (WDMD). Unlike the Mahalanobis distance (MD), which is constructed by fusing original features directly, the WDMD is formed with some selected features only, and the features are weighted before integration. Second, an exponential model is developed to describe the degradation path of the ball screw system. Then, the particle filtering algorithm is employed to combine the WDMD and the degradation model for state estimation and RUL prediction. The proposed approach is verified by a dataset obtained from an experimental system designed for accelerated life tests of the ball screw system. The results show that the WDMD has a more apparent deterioration trend than the MD and the proposed exponential model performs better than both the linear model and the nonlinear model in RUL prediction

A locally adaptive ensemble approach for data-driven prognostics of heterogeneous fleets

In this work, we consider the problem of predicting the remaining useful life of a piece of equipment, based on data collected from a heterogeneous fleet working under different operating conditions. When the equipment experiences variable operating conditions, individual data-driven prognostic models are not able to accurately predict the remaining useful life during the entire equipment life. The objective of this work is to develop an ensemble approach of different prognostic models for aggregating their remaining useful life predictions in an adaptive way, for good performance throughout the degradation progression. Two data-driven prognostic models are considered, a homogeneous discrete-time finite-state semi-Markov model and a fuzzy similarity-based model. The ensemble approach is based on a locally weighted strategy that aggregates the outcomes of the two prognostic models of the ensemble by assigning to each model a weight and a bias related to its local performance, that is, the accuracy in predicting the remaining useful life of patterns of a validation set similar to the one under study. The proposed approach is applied to a case study regarding a heterogeneous fleet of aluminum electrolytic capacitors used in electric vehicle powertrains. The results have shown that the proposed ensemble approach is able to provide more accurate remaining useful life predictions throughout the entire life of the equipment compared to an alternative ensemble approach and to each individual homogeneous discrete-time finite-state semi-Markov model and fuzzy similarity-based models

Bilinear and parallel prediction methods

To make accurate predictions about a system one must develop a model for that system. Bilinear models are often attractive options because they allow the user to model nonlinear interactions between variables in complicated systems with (potentially) millions of variables. In this work we apply bilinear models to two separate domains and present novel models for improved prediction accuracy and novel heuristics for solving the optimization problems that arise from the use of bilinear models. In the first system we use a bilinear model to predict the remaining useful life (RUL) of a rechargeable lithium-ion (Li-ion) battery. The approach used to solve the bilinear model leverages bilinear kernel regression to build a nonlinear mapping between the capacity feature space and the RUL state space. Specific innovations of the approach include: a general framework for robust sparse prognostics that effectively incorporates sparsity into kernel regression and implicitly compensates for errors in capacity features; and two numerical procedures for error estimation that efficiently derives optimal values of the regression model parameters. Second, we apply a bilinear model to the matrix completion problem, where one seeks to recover a data matrix from a small sample of observed entries. We assume the matrix we wish to recover is low-rank (the rank of the matrix is much less than either dimension) and model it as the product of two low-rank matrices. We then adapt existing parallel solutions to this bilinear model for use on a graphics processing unit (GPU). Additionally, we introduce a novel method for inductive matrix completion on a GPU

Bearing prognostics using neural network under time varying conditions

Condition based maintenance (CBM) aims to schedule maintenance activities based on condition monitoring data in order to lower the overall maintenance costs and prevent unexpected failures. Effective CBM can lead to reduced downtime, less inventory, reduced maintenance costs, reliable operation and safety of entire system. The key challenge in achieving effective CBM is the accurate prediction of equipment future health condition and thus the remaining useful life. Existing prognostics methods mainly focus on constant loading conditions. However, in many applications, such as some wind turbine, transmission and engine applications, the load that the equipment is subject to changes over time. It is critical to incorporate the changing load in order to produce more accurate prognostics methods. This research is focused on the bearing prognostics, which are key mechanical components in rotary machines, supporting the entire load imposed on machines. Failure of these components can stop the operation due to machine down time, thus resulting in financial losses, which are much higher than the cost of bearing. In this thesis, an artificial neural network (ANN) based method is proposed for equipment health condition prediction under time varying conditions. The proposed method can be applied to bearing as well as other components under condition monitoring. In the proposed ANN model, in addition to using the age and condition monitoring measurement values as an inputs, a new input neuron is introduced to incorporate the varying loading condition. The output of the ANN model is accumulated life percentage, based on which the remaining useful life can be calculated once the ANN is trained. Two sets of simulated degradation data under time varying load are used to demonstrate the effectiveness of the proposed ANN method, and the results show that fairly accurate prediction can be achieved using the proposed method. The other key contribution of this thesis is the experiment validation of the proposed ANN prediction method. The Bearing Prognostics Simulator, after extensive adjustment and tuning, is used to perform bearing run-to-failure test under different loading conditions. Vibration signals are collected using the data acquisition system and the Labview software. The root mean square (RMS) measurement of the vibration signals is used as the condition monitoring input for the validation of the proposed ANN prediction method. Two bearing failure histories are used to train the ANN model and test its prediction performance. The results demonstrate the effectiveness of the proposed method in dealing with real-world condition monitoring data for health condition prediction. The proposed model can greatly benefit industry as well as academia in condition based maintenance of rotary machines