A Novel Hybrid Method of Parameters Tuning in Support Vector Regression for Reliability Prediction: Particle Swarm Optimization Combined With Analytical Selection

Support vector regression (SVR) is a widely used technique for reliability prediction. The key issue for high prediction accuracy is the selection of SVR parameters, which is essentially an optimization problem. As one of the most effective evolutionary optimization methods, particle swarm optimization (PSO) has been successfully applied to tune SVR parameters and is shown to perform well. However, the inherent drawbacks of PSO, including slow convergence and local optima, have hindered its further application in practical reliability prediction problems. To overcome these drawbacks, many improvement strategies are being developed on the mechanisms of PSO, whereas there is little research exploring a priori information about historical data to improve the PSO performance in the SVR parameter selection task. In this paper, a novel method controlling the inertial weight of PSO is proposed to accelerate its convergence and guide the evolution out of local optima, by utilizing the analytical selection (AS) method based on a priori knowledge about SVR parameters. Experimental results show that the proposed ASPSO method is almost as accurate as the traditional PSO and outperforms it in convergence speed and ability in tuning SVR parameters. Therefore, the proposed ASPSO-SVR shows promising results for practical reliability prediction tasks

A Prediction Modeling Framework For Noisy Welding Quality Data

Numerous and various research projects have been conducted to utilize historical manufacturing process data in product design. These manufacturing process data often contain data inconsistencies, and it causes challenges in extracting useful information from the data. In resistance spot welding (RSW), data inconsistency is a well-known issue. In general, such inconsistent data are treated as noise data and removed from the original dataset before conducting analyses or constructing prediction models. This may not be desirable for every design and manufacturing applications since every data can contain important information to further explain the process. In this research, we propose a prediction modeling framework, which employs bootstrap aggregating (bagging) with support vector regression (SVR) as the base learning algorithm to improve the prediction accuracy on such noisy data. Optimal hyper-parameters for SVR are selected by particle swarm optimization (PSO) with meta-modeling. Constructing bagging models require 114 more computational costs than a single model. Also, evolutionary computation algorithms, such as PSO, generally require a large number of candidate solution evaluations to achieve quality solutions. These two requirements greatly increase the overall computational cost in constructing effective bagging SVR models. Meta-modeling can be employed to reduce the computational cost when the fitness or constraints functions are associated with computationally expensive tasks or analyses. In our case, the objective function is associated with constructing bagging SVR models with candidate sets of hyper-parameters. Therefore, in regards to PSO, a large number of bagging SVR models have to be constructed and evaluated, which is computationally expensive. The meta-modeling approach, called MUGPSO, developed in this research assists PSO in evaluating these candidate solutions (i.e., sets of hyper-parameters). MUGPSO approximates the fitness function of candidate solutions. Through this method, the numbers of real fitness function evaluations (i.e., constructing bagging SVR models) are reduced, which also reduces the overall computational costs. Using the Meta2 framework, one can expect an improvement in the prediction accuracy with reduced computational time. Experiments are conducted on three artificially generated noisy datasets and a real RSW quality dataset. The results indicate that Meta2 is capable of providing promising solutions with noticeably reduced computational costs

Fault Diagnosis and Failure Prognostics of Lithium-ion Battery based on Least Squares Support Vector Machine and Memory Particle Filter Framework

123456A novel data driven approach is developed for fault diagnosis and remaining useful life (RUL) prognostics for lithium-ion batteries using Least Square Support Vector Machine (LS-SVM) and Memory-Particle Filter (M-PF). Unlike traditional data-driven models for capacity fault diagnosis and failure prognosis, which require multidimensional physical characteristics, the proposed algorithm uses only two variables: Energy Efficiency (EE), and Work Temperature. The aim of this novel framework is to improve the accuracy of incipient and abrupt faults diagnosis and failure prognosis. First, the LSSVM is used to generate residual signal based on capacity fade trends of the Li-ion batteries. Second, adaptive threshold model is developed based on several factors including input, output model error, disturbance, and drift parameter. The adaptive threshold is used to tackle the shortcoming of a fixed threshold. Third, the M-PF is proposed as the new method for failure prognostic to determine Remaining Useful Life (RUL). The M-PF is based on the assumption of the availability of real-time observation and historical data, where the historical failure data can be used instead of the physical failure model within the particle filter. The feasibility of the framework is validated using Li-ion battery prognostic data obtained from the National Aeronautic and Space Administration (NASA) Ames Prognostic Center of Excellence (PCoE). The experimental results show the following: (1) fewer data dimensions for the input data are required compared to traditional empirical models; (2) the proposed diagnostic approach provides an effective way of diagnosing Li-ion battery fault; (3) the proposed prognostic approach can predict the RUL of Li-ion batteries with small error, and has high prediction accuracy; and, (4) the proposed prognostic approach shows that historical failure data can be used instead of a physical failure model in the particle filter

A Particle Swarm-optimized Support Vector Machine for Reliability Prediction

Article first published online: 22 JUN 2011International audienceSystem reliability depends on inherent mechanical and structural aging factors as well as on operational and environmental conditions, which could enhance (or smoothen) such factors. In practice, the involved dependences may burden the modeling of the reliability behavior over time, in which traditional stochastic modeling approaches may likely fail. Empirical prediction methods, such as support vector machines (SVMs), become a valid alternative whenever reliable time series data are available. However, the prediction performance of SVMs depends on the setting of a number of parameters that influence the effectiveness of the training stage during which the SVMs are constructed based on the available data set. The problem of choosing the most suitable values for the SVM parameters can be framed in terms of an optimization problem aimed at minimizing a prediction error. In this work, this problem is solved by particle swarm optimization (PSO), a probabilistic approach based on an analogy with the collective motion of biological organisms. SVM in liaison with PSO is then applied to tackle reliability prediction problems based on time series data of engineered components. Comparisons of the obtained results with those given by other time series techniques indicate that the PSO+SVM model is able to provide reliability predictions with comparable or great accuracy

Damage Precursor Based Structural Health Monitoring and Prognostic Framework Using Dynamic Bayesian Network

Structural health monitoring (SHM), as an essential tool to ensure the health integrity of aging structures, mostly focus on monitoring conventional observable damage markers such as fatigue crack size. However, degradation starts and progressively evolves at microstructural levels much earlier than detection of such indicators. This dissertation goes beyond classical approaches and presents a new SHM framework based on evolution of Damage Precursors, when conventional direct damage indicator, such as crack, is unobservable, inaccessible or difficult to measure. Damage precursor is defined in this research as “any detectable variation in material/ physical properties of the component that can be used to infer the evolution of the hidden/ inaccessible/ unmeasurable damage during the degradation”. Accordingly, the degradation process is to be expressed based on progression of damage precursor through time and the damage state assessment would be updated by incorporating multiple different evidences. Therefore, this research proposes a systematic integration approach through Dynamic Bayesian Network (DBN) to include all the evidences and their relationships. The implementation of augmented particle filtering as a stochastic inference method inside DBN enables estimating both model parameters and damage states simultaneously in light of various evidences. Incorporating different sources of information in DBN entails advance techniques to identify and formulate the possible interaction between potentially non-homogenous variables. This research uses the Support Vector Regression (SVR) in order to define generally unknown nonparametric and nonlinear correlation between some of the variables in the DBN structure. Additionally, the particle filtering algorithm is studied more fundamentally in this research and a modified approach called “fully adaptive particle filtering” is proposed with the idea of online updating not only the state process model but also the measurement model. This new approach improves the ability of SHM in real-time diagnostics and prognostics. The framework is successfully applied to damage estimation and prediction in two real-world case studies of 1) crack initiation in a metallic alloy under fatigue and, 2) damage estimation and prognostics in composite materials under fatigue. The proposed framework is intended to be general and comprehensive such that it can be implemented in different applications