Steve Vandenplas

Kalman-Filtering-Based Prognostics for Automatic Transmission Clutches

Demands of low-cost prognostics tool for automatic transmission clutches (i.e., based on measurement data from sensors typically available) by industry have increased since the last few years. In this paper, a prognostics tool is developed by fusing a newly developed degradation model with the measurable pre-lockup feature under the extended Kalman filtering framework. As this feature can be extracted from sensory data typically available in wet clutch applications, the developed prognostics tool, hence, does not require extra cost for any additional sensor. New history data of commercially available wet clutches obtained from accelerated life tests using a fully instrumented SAE#2 test setup have been acquired and processed. The experimental results show that the prognostics algorithm developed in this paper outperforms the early developed prognostics algorithm, which is based on the weighted mean slope method (i.e., data-driven approach). It is shown that the clutch remaining useful life estimations with the novel prognostics algorithm remain in the desired accuracy region of 20% with relatively small uncertainty interval in comparison with the early developed prognostics algorithm.

Condition-based maintenance for OEM’s by application of data mining and prediction techniques

Increasing the products service life and reducing the number of service visits are becoming more and more top priorities for Original Equipment Manufacturer companies (OEM’s). Condition-based maintenance is often proposed as a solution to reach this goal. However, this latter, is often hampered by the lack of the right information that gives a good indication of the health of the equipment. Furthermore, the processing power needed to compute this information is often not afforded by machine’s processor. In this paper, a remote platform which connects the OEM’s to the customer’s premises is described, allowing thus a local computation of available information. Two approaches are then combined to process the optimal maintenance time. First, data mining techniques and reliability estimation are applied to historical databases of machines running in the field in order to extract the relevant features together with their associated thresholds. Second, prediction algorithm is applied to the selected features in order to estimate the optimal time to preventively perform a maintenance action. The proposed method has been applied to a database of more than 2000 copy machines running in the field and proved to identify easily the relevant features to be forecasted and to offer an accurate prediction of the maintenance action.

Driver Modeling for Heavy Hybrid Vehicle Energy Management

The paper presents an approach for modeling and predicting the user intentions with application for optimization of the hybrid electrical vehicle. An auto-regressive moving-average model isdesigned to model and predict the driver behavior. The resulting model is converted to a Markov-chain model and used with stochastic dynamic programming, which optimizes the gear-shifting and the power split between the internal combustion engine and the electrical storage of a hybrid electrical vehicle. Verification of resulting energy efficiency is performed using real-life driving data from a heavy-duty industrial vehicle (forklift).

A fast pick-and-place prototype robot: design and control

This article focuses on a newly built research platform (a robot). Using its vision system, it can identify round objects that are randomly scattered on a table. Then, using its gripper they are picked and placed inside a basket. The control system is tuned such that, the succession of operations runs fast and safe. In this paper we present how this has been achieved: going from concepts to design, validation in simulation and eventually experimental validation. Lessons learned can save time for other parties interested in building prototype robots.

Acoustic emission monitoring using a multimode optical fiber sensor

Permanent damage in various materials and constructions often causes high-energy high-frequency acoustic waves. To detect those so called ‘acoustic emission (AE) events’, in most cases ultrasonic transducers are embedded in the structure or attached to its surface. However, for many applications where event localization is less important, an embedded low-cost multimode optical fiber sensor configured for event counting may be a better alternative due to its corrosion resistance, immunity to electromagnetic interference and light-weight. The sensing part of this intensity-modulated sensor consists of a multimode optical fiber. The sensing principle now relies on refractive index variations, microbending and mode-mode interferences by the action of the acoustic pressure wave. A photodiode is used to monitor the intensity of the optical signal and transient signal detection techniques (filtering, frame-to-frame analysis, recursive noise estimation, power detector estimator) on the photodiode output are applied to detect the events. In this work, the acoustic emission monitoring capabilities of the multimode optical fiber sensor are demonstrated with the fiber sensor embedded in the liner of a Power Data Transmission (PDT) coil to detect damage (delamination, matrix cracking and fiber breaking) while bending the coil. With the Hankel Total Least Square (HTLS) technique, it is shown that both the acoustic emission signal and optical signal can be modeled with a sum of exponentially damped complex sinusoids with common poles.

A robust optimal nonlinear control for uncertain systems: Application to a robot manipulator

The aim of the paper is twofold. Firstly, in this paper we are interested in control design for a class of uncertain MIMO linear systems. Specifically, we deal with trajectory tracking problems. Using the explicit nonlinear control law developed by Leitmann and co-authors during 1980s, the contribution of this paper is to elaborate upon useful closed-loop properties: (i) robustness, namely how to dominate the negative effect of the uncertainty; (ii) optimality with respect to an infinite time-horizon cost function; (iii) state estimation, ensuring uniform boundedness of the error between real and estimated states. Towards practical implementation, the control designer can choose to tune the nonlinear control law parameters based on robustness or optimality requirements. This flexibility distinguishes this article from others in the literature. The second aim of this paper is to apply, in particular, the aforementioned theoretical results on a two degree-of-freedom, rigid link, robot manipulator. Our intention is to contribute to the need for advanced model-based robot control.

An ECMS-based powertrain control of a parallel hybrid electric forklift

In this paper we focus on the supervisory control problem of a parallel hybrid electric vehicle (HEV): minimize fuel consumption while ensuring self-sustaining State-of-Charge (SoC). We reapply the state of the art methodology by comparing optimal results of Dynamic Programming (DP) against a real-time control candidate. After careful selection, we opted for an Equivalent Consumption Minimization Strategy (ECMS) based approach for the following reasons: (i) results are quite remarkable with less than 5% fuel usage increase when compared to DP; (ii) simple and intuitive tuning of control parameters; (iii) readily usable for code generation (prototyping). Topics that distinguish this article from others in the literature include: (i) the usage of trapezoidal rule of integration implementing DP and ECMS; consequently, the offline simulation results are intended to be more precise and representative when compared against the more common, often used rectangular rule; (ii) a particular post-processing procedure of the recorded driving cycle data based on physical interpretation; it allows consistent offline simulations with quite high sampling period (in the order of seconds); (iii) tuning of control parameters in such a way that control system is robust towards new, unknown, unpredictable but closely resembling driving cycles. In particular, we focus on the supervisory control of a forklift truck. The real-time control is able to compute: (i) the power split (i.e. a balanced usage between an internal combustion engine and a supercapacitor); (ii) the drivetrain control (i.e. automatic gear shifting and clutching). Numerous numerical implementation issues are discussed along our presentation.

Incremental Classifier Fusion and Its Applications in Industrial Monitoring and Diagnostics

Pattern recognition techniques have shown their usefulness for monitoring and diagnosing many industrial applications. The increasing production rates and the growing databases generated by these applications require learning techniques that can adapt their models incrementally, without revisiting previously used data. Ensembles of classifiers have been shown to improve the predictive accuracy as well as the robustness of classification systems. In this work, several well-known classifier fusion methods (Fuzzy Integral, Decision Templates, Dempster–Shafer Combination, and Discounted Dempster–Shafer Combination) are extended to allow incremental adaptation. Additionally, an incremental classifier fusion method using an evolving clustering approach is introduced—named Incremental Direct Cluster-based ensemble. A framework for strict incremental learning is proposed in which the ensemble and its member classifiers are adapted concurrently. The proposed incremental classifier fusion methods are evaluated within this framework for two industrial applications: online visual quality inspection of CD imprints and prediction of maintenance actions for copiers from a large historical database.