Prognostic Health Monitoring Research Articles

Comparison of Prognostic Health Management Algorithms for Assessment of Electronic Interconnect Reliability Under Vibration

This paper compares three prognostic algorithms applied to the same data recorded during the failure of a solder joint in ball grid array component attached to a printed circuit board. The objective is to expand on the relative strengths and weaknesses of each proposed algorithm. Emphasis will be placed on highlighting differences in underlying assumptions required for each algorithm, details of remaining useful life calculations, and methods of uncertainty quantification. Metrics tailored specifically for prognostic health monitoring (PHM) are presented to characterize the performance of predictions. The relative merits of PHM algorithms based on a Kalman filter, extended Kalman filter, and a particle filter all demonstrated on the same data set will be discussed. The paper concludes by discussing which algorithm performs best given the information available about the system being monitored.

Real-time Sequential Monte Carlo Sampling based on a Committee of Artificial Neural Networks for Residual Lifetime Prediction of a Component Subjected to Fatigue Crack Growth

Most of the studies available in the literature about sequential Monte-Carlo sampling algorithm assume that a sufficient number of process observations is available, to guarantee the convergence of the algorithm on the target process evolution. This requirement is remotely met if the process of fatigue crack growth is concerned, due to the costs of maintenance procedures, especially within the aeronautical field. A real-time diagnostic system is the enabler of the prognostic health monitoring methodology.This work is about the application of sequential Monte-Carlo sampling to estimate the probabilistic residual lifetime of a monitored structural component, subjected to fatigue crack propagation. A real-time diagnostic unit for crack detection and damage assessment, trained with Finite Element simulations of damage evolution, generates the information as input to the prognostic unit. A crack propagation model provides the knowledge of the residual lifetime prior to the application of fatigue loads. The prognostic unit updates in real-time the probability density functions of the component residual lifetime at each discrete time during fatigue crack evolution. The methodology is preliminarily applied in simulated environment to an aeronautical metallic panel and the overall performance of a fully autonomous prognostic health monitoring system based upon simulated strain measures is evaluated.0

An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process

Efficient asset management is of paramount importance, particularly for systems with costly downtime and failure. As in energy and capital-intensive industries, the economic loss of downtime and failure is huge, the need for a low-cost and integrated health monitoring system has increased significantly over the years. Timely detection of faults and failures through an efficient prognostics and health management (PHM) framework can lead to appropriate maintenance actions to be scheduled proactively to avoid catastrophic failures and minimize the overall maintenance cost of the systems. This paper aims at practical challenges of online diagnostics and prognostics of mechanical systems under unobservable degradation. First, the elements of a multistate degradation structure are reviewed and then a model selection framework is introduced. Important dynamic performance measures are introduced, which can be used for online diagnostics and prognostics. The effectiveness of the result of this paper is demonstrated with a case study on the health monitoring of turbofan engines.

Advanced Vibration Sensing with Radar - ADVISER


 
 
 A new low-cost stand-off vibration sensor based on the Doppler radar principle is presented. The baseline performance of this sensor was compared with a high-quality accelerometer in a well-controlled laboratory environment. This advanced vibration imaging sensor (ADVISER) was also validated for its prognostic health monitoring ability with a fault emulator. The ADVISER was able to detect machine misbalance and bearing damage at a distance of 4 feet without making any contact. This exceeded the performance of a high-quality screw accelerometer mounted directly on the bearing enclosure. In this paper, we present the sensor’s principle of operation, summarize results of comparing it with standard accelerometers, and conclude with its potential use in industrial and aerospace applications.
 
 

A Hybrid Prognostic Model Formulation and Health Estimation of Auxiliary Power Units

Prognostic health monitoring is an important element of condition-based maintenance and logistics support. The accuracy of prediction and the associated confidence in prediction greatly influence overall performance and subsequent actions either for maintenance or logistics support. Accuracy of prognosis is directly dependent on how closely one can capture the system and component interactions. Traditionally, such models assume a constant and univariate prognostic formulation—that is, components degrade at a constant rate and are independent of each other. Our objective in this paper is to model the degrading system as a collection of prognostic states (health vectors) that evolve continuously over time. The proposed model includes an age dependent deterioration distribution, component interactions, as well as effects of discrete events arising from line maintenance actions and/or abrupt faults. Mathematically, the proposed model can be summarized as a continuously evolving dynamic model, driven by non-Gaussian input and switches according to the discrete events in the system. We develop this model for aircraft auxiliary power units, but it can be generalized to other progressive deteriorating systems. The system identification and recursive state estimation scheme for the developed non-Gaussian model under a partially specified distribution framework has been deduced. The diagnostic/prognostic capabilities of our model and algorithms have been demonstrated using simulated and field data.

Survey: State of the Art in NDE Data Fusion Techniques

As the requirements on the accuracy of nondestructive testing and evaluation are increasing, multiple nondestructive evaluation (NDE) methods are often employed to increase the reliability and reduce the uncertainty of the testing and evaluation. The need to process and analyze data from multiple sources uses the technique named data fusion, which has been growing rapidly in the NDE community in recent years. This paper reviews the progress in NDE data fusion techniques and examines the mathematical fusion algorithms, which include but are not limited to optimization methods, multiresolution approaches, heuristic methods, probabilistic methods, and scientific visualization. In the light of recent applications in NDE data fusion, important issues in handling the acquired data and applying the fusion algorithms are identified and discussed. A generic framework to apply the NDE data fusion process is described. The study on fusion of advanced NDE and sensor techniques opens up prospects for the diagnostic and prognostic health monitoring applications in the future.