Time-Domain Data Fusion Using Weighted Evidence and Dempster–Shafer Combination Rule: Application in Object Classification

To apply data fusion in time-domain based on Dempster–Shafer (DS) combination rule, an 8-step algorithm with novel entropy function is proposed. The 8-step algorithm is applied to time-domain to achieve the sequential combination of time-domain data. Simulation results showed that this method is successful in capturing the changes (dynamic behavior) in time-domain object classification. This method also showed better anti-disturbing ability and transition property compared to other methods available in the literature. As an example, a convolution neural network (CNN) is trained to classify three different types of weeds. Precision and recall from confusion matrix of the CNN are used to update basic probability assignment (BPA) which captures the classification uncertainty. Real data of classified weeds from a single sensor is used test time-domain data fusion. The proposed method is successful in filtering noise (reduce sudden changes—smoother curves) and fusing conflicting information from the video feed. Performance of the algorithm can be adjusted between robustness and fast-response using a tuning parameter which is number of time-steps(ts)

Paradox Elimination in Dempster–Shafer Combination Rule with Novel Entropy Function: Application in Decision-Level Multi-Sensor Fusion

Multi-sensor data fusion technology in an important tool in building decision-making applications. Modified Dempster–Shafer (DS) evidence theory can handle conflicting sensor inputs and can be applied without any prior information. As a result, DS-based information fusion is very popular in decision-making applications, but original DS theory produces counterintuitive results when combining highly conflicting evidences from multiple sensors. An effective algorithm offering fusion of highly conflicting information in spatial domain is not widely reported in the literature. In this paper, a successful fusion algorithm is proposed which addresses these limitations of the original Dempster–Shafer (DS) framework. A novel entropy function is proposed based on Shannon entropy, which is better at capturing uncertainties compared to Shannon and Deng entropy. An 8-step algorithm has been developed which can eliminate the inherent paradoxes of classical DS theory. Multiple examples are presented to show that the proposed method is effective in handling conflicting information in spatial domain. Simulation results showed that the proposed algorithm has competitive convergence rate and accuracy compared to other methods presented in the literature

Analytical Redundancy Based Predictive Fault Tolerant Control of a Steer-By-Wire System Using Nonlinear Observer

poster abstractA nonlinear observer based analytical redundancy methodology is presented for fault tolerant control of a steer by wire (SBW) system. A long-range predictor based on Diophantine identity has been utilized to improve the fault detection efficiency. The overall predictive fault tolerant control strategy was then implemented and validated on a steer by wire hardware in loop bench. The experimental results showed that the overall robustness of the SBW system was not sacrificed through the usage of analytical redundancy for sensors along with the designed fault detection algorithm. Moreover, the experimental results indicate that the faults could be detected faster using the developed analytical redundancy based algorithms for attenuating-type faults

Fuzzy Pattern Classification Based Detection of Faulty Electronic Fuel Control (EFC) Valves Used in Diesel Engines

In this paper, we develop mathematical models of a rotary Electronic Fuel Control (EFC) valve used in a Diesel engine based on dynamic performance test data and system identification methodology in order to detect the faulty EFC valves. The model takes into account the dynamics of the electrical and mechanical portions of the EFC valves. A recursive least squares (RLS) type system identification methodology has been utilized to determine the transfer functions of the different types of EFC valves that were investigated in this study. Both in frequency domain and time domain methods have been utilized for this purpose. Based on the characteristic patterns exhibited by the EFC valves, a fuzzy logic based pattern classification method was utilized to evaluate the residuals and identify faulty EFC valves from good ones. The developed methodology has been shown to provide robust diagnostics for a wide range of EFC valves

An Electrical Capacitance Tomography Based Soot Load Estimation Method for a Diesel Particulate Filter

poster abstractThis research presents a novel approach of Particulate Material (soot) measurement in a Diesel particulate filter using Electrical Capacitance Tomography. Modern Diesel Engines are equipped with Diesel Particulate Filters (DPF’s), as well as on-board technologies to evaluate the status of DPF because complete knowledge of DPF soot loading is very critical for robust efficient operation of the engine exhaust after treatment system. Emission regulations, getting stringent day by day, imposed upon all internal combustion engines, including Diesel engines on gaseous as well as particulates (soot) emissions by Environment Regulatory Agencies. In course of time, soot will be deposited inside the DPFs which tend to clog the filter and hence generate a back pressure in the exhaust system, negatively impacting the fuel efficiency. To remove the soot build-up, regeneration (active or passive) of the DPF must be done as an engine exhaust after treatment process at pre-determined time intervals. Since the regeneration process consume fuel, a robust and efficient operation based on accurate knowledge of the particulate matter deposit (or soot load) becomes essential in order to keep the fuel consumption at a minimum. In this paper, we propose a sensing method for a DPF that can accurately measure in-situ soot load using Electrical Capacitance Tomography (ECT). Simulation results show that the proposed method offers an effective way to accurately estimate the soot load in DPF. A hardware-in-loop bench has been built in the Mechatronics Research Lab at IUPUI to further develop this sensing concept and experimentally verify the associated measurement technology. Preliminary experimental data is very promising. This poster will present this novel sensing concept and some of the experimental results that support this technology. A patent application has been filed on this technology by IURTC in January, 2014. The proposed method is expected to have a profound impact in improving overall PM filtering efficiency (and thereby fuel efficiency), and durability of a Diesel Particulate Filter (DPF) through appropriate closed loop regeneration operation

Reconfiguration of a Wind Turbine with Hydrostatic Drivetrain to Improve Annual Energy Production

Abstract: Hydrostatic transmission systems (HTS) have shown potential in replacing gearbox in conventional wind turbines. However, the general perception about these systems is that they suffer from low efficiencies, specifically at low wind speeds. This paper presents a novel technique that can improve the annual energy production (AEP) beyond that of a conventional wind turbine. By optimizing the operating conditions and the design of the wind turbine, the performance and efficiency of a HTS can be improved. A side-by-side comparison with the conventional wind turbines is provided to highlight the benefits of the proposed methodology. One of the findings of this research is that, rotor, hydrostatic pump, motor and their operations' planning must be optimized together to achieve higher AEP. The reconfigured turbines are shown to provide up to 8 percent AEP increase for a 750 kW plant and up to 10 percent increase for 1500 kW plants using the proposed drivetrain configurations

Statistical significance of rank regression

Rank regression, which is quite simple to use some form of monotonic relationship between X and Y. Since the rank regression is a nonparametric approach so there are essentially no confidence interval, hypothesis tests, prediction intervals, and interpretation of regression coefficients. In this article, we proposed a bootstrap statistical significance measure of the rank regression by formulating a bootstrap interval for the rank regression parameters. If the rank regression parameters from the original data are not within the bootstrap interval, the rank regression parameters are considered significance. Numerical examples show that the merit of using this proposed bootstrap interval

Application of Adaptive Estimation Techniques on Battery Fault Diagnosis

poster abstractHigh energy storage systems like Li-ion Batteries are one of the most widely used renewable energy sources today. They find applications in everyday electronic gadgetry, critical medical devices, hybrid & electric vehicles to name a few. Our study aims to observe continuously the state of the Li-ion battery and detect Over Charge (OC) and Over Discharge (OD) failures occurring in real time. Both conditions are detrimental to the health of the battery, while over charge can lead to overheating and thus vaporization of active material and hence explosion, over discharge can short the battery cell. However, these types of failures can be detected before they occur and by raising a flag before the system reaches the failure condition such failure modes can be avoided. Different battery models based on equivalent circuit approach are constructed using the impedance spectroscopy data from Li-ion battery cells. Kalman filters are used to estimate the state of each system and subsequent residuals are generated for each model. Multiple model adaptive estimation is then used, where the generated residuals are evaluated and the fault probabilities are generated. Based on these probabilities, the system is classified as normal operation, OC fault or OD fault. Simulation results show that the battery faults can be detected and diagnosed in real time, thereby proving to an effective way of Li-ion battery fault diagnosis

Agent-based Three Layer Framework of Assembly-Oriented Planning and Scheduling for Discrete Manufacturing Enterprises

To solve the cost burden caused by delivery tardiness for small and medium sized packaging machinery enterprises, the assembly-oriented planning and scheduling is studied based on the multi-agent technology. Taking into account the due date, the planning and scheduling are optimized iteratively with the rule-based algorithms complying with the machining and assembling process constraints. To make the realistic large-scale production planning scheme tailored to fit the optimization algorithms, a multi-agent system is utilized to conceptually construct a three-layer framework corresponding to three planning horizons of different task level. These planning horizons are quarter/month of product/subassembly level, week of part level, and day of operation level. With the strategy of combining top-down task decomposition and bottom-up plan update (where the quarterly orders are decomposed into the monthly, weekly, and daily tasks according to the product processing structure while the resulting plans of every layer are updated iteratively based on the bottom layer optimization), the proposed framework not only addresses the planning but also the periodic and event-driven scheduling of the processes. In this paper, a gravure printing machine is considered as a test case for evaluating the proposed framework. The simulation results with the historical data of the orders for this machine demonstrate the effectiveness of the proposed scheme on the control of the distribution of idle-time. It can also provide a resolution to tardiness penalty for small and medium sized enterprises