Hassene Jammoussi

Statistics Based Detection and Isolation of UEGO Sensor Faults

Stringent emission regulations mandated by California air regulation board (CARB) require monitoring the upstream exhaust gas oxygen (UEGO) sensor for any possible malfunction causing the vehicle emissions to exceed certain thresholds. Six faults have been identified to potentially cause the UEGO sensor performance to deteriorate and potentially lead to instability of the air-fuel ratio (AFR) control loop. These malfunctions are either due to an additional delay or an additional lag in the transition of the sensor response from lean to rich or rich to lean. Current technology detects the faults the same way (approximated by a delay type fault) and does not distinguish between the different faults. In the current paper, a statistics based approach is developed to diagnose these faults. Specifically, the characteristics of a non-normal distribution function are estimated based on the UEGO sensor output and used to detect and isolate the faults. When symmetric operation is detected, a system identification process is employed to estimate the parameters of the dynamic system and determine the type of operation. The proposed algorithm has been demonstrated on real data obtained from both Ford F150 and Mustang V6 vehicles.© 2013 ASME

Diagnostics of Oxygen Sensors and Air-Fuel Ratio Adaptive Controls

Stringent emission regulations mandated by California air regulation board (CARB) require monitoring the upstream exhaust gas oxygen (UEGO) sensor for any possible malfunction causing the vehicle emissions to exceed standard thresholds. Six faults have been identified that may potentially cause the UEGO sensor performance to deteriorate and lead to instability of the air-fuel ratio (AFR) closed-loop control system. These malfunctions are either due to an additional delay or an additional lag in the transition of the sensor response from lean to rich or rich to lean. In this paper, a novel non-intrusive approach is developed to diagnose these faults using a combination of a statistical method and a system identification process. In the second part of this work, a control strategy is presented that utilizes the type, the direction and the magnitude of the fault present to update the gains of the controller for the closed-loop air-fuel ratio control system. The proposed strategy does not require modifying the controller structure and only adapts the baseline gains of the controller and delay compensator to match the actual system dynamics (in presence of fault). The proposed approach has been demonstrated on a vehicle (Mustang V6 3.7L) where different faults were induced, and the emissions associated with each fault were measured to show the improvement.Copyright

Adaptive Air Fuel Ratio Controls in Presence of Oxygen Sensor Faults

Fault monitoring of the upstream universal exhaust gas oxygen (UEGO) sensor, as mandated by the California air resources board (CARB), is a necessary action to maintain the performance of the operation of the air-fuel ratio (AFR) control system and indicate the need for maintenance when a fault is present which could potentially lead to exceeding the emissions limits. When the UEGO sensor fault is accurately diagnosed, i.e. fault is detected, direction is identified and magnitude is estimated, tuning of the controller gains can be performed accurately with minimal calibration efforts. Presented in this paper is a control strategy that utilizes the type, direction and magnitude of fault detected to adapt the gains of the controller and update the parameters of the Smith predictor (SP) in order to maintain the stability of AFR control loop. The proposed approach has been validated on a vehicle (Mustang V6 3.7L) equipped with ATI No-Hooks rapid prototyping system. Different fault types and magnitudes were tested and the tailpipe emissions were assessed on federal test procedure (FTP) cycles.Copyright

Air filter diagnostics & prognostics in naturally aspired engines

Air filter diagnostics/prognostics are needed to determine the condition of the intake air filter in internal combustion engines and to indicate the need for maintenance. Monitoring air filter health helps saving on operational costs, maintaining optimal fuel economy, and planning for service/maintenance before failure. In the present paper, we propose an air filter diagnostics/prognostics method that relies on air flow models and control volume approach to assess filter health and estimate downstream pressure. The development is needed for non-boosted applications where a pressure sensor is generally not present downstream the air filter. Real data validation is conducted using engine data from an experimental vehicle. Experimental results show the effectiveness of the proposed approach to monitor air filter health.

Model Based Control of a Diesel Engine

Proposed in this paper is an automated framework for calibration of diesel engine governors. The process involves two basic parts, online engine model identification followed by governor gain design. A previously developed Instrumental Variable 3 Step Algorithm for closed loop system identification is used to estimate the engine model. The identified model is then used in two different governor calibration approaches. The first approach employs a typical governor structure involving acceleration feedback. It will be shown that this governor structure reduces to a classical two degree-of-freedom design. The second approach is based on a procedure in which a desired open-loop transfer function (target transfer function) is shaped such that the same performance specifications as for the first design are satisfied. The control design methods are applied for an off-highway diesel engine with a disengaged transmission. In-field data collected from the engine operating closed-loop is used to identify a model for the open-loop system and the controller gains are then determined. The loop shaping method is then applied to the identified model to design a feedback controller and a prefilter. The efficacy of both loops in terms of tracking performance and noise rejection has been demonstrated through a time domain simulation of both closed-loop step responses.Copyright

Modeling and Diagnostics of Heart Assist Devices

Presented are online adaptive models for ventricular assist devices (VADs). Such devices are used to assist failing hearts or in the case considered here to create a total artificial heart. Adaptive models are developed to estimate cardiac output (CO) and power consumption of the VAD. These parameters are critical to physicians during patient care as well as in diagnosing VAD operation. The online adaptive nature of these models will be used to estimate effective blood viscosity in real-time and to create a mechanism whereby specific VAD diagnostics, important to robust CO delivery, can be identified, isolated and estimated. The experiments conducted were ex-vivo in a mock circulation loop in which the precise nature of the working fluid properties can be controlled and measured.Copyright

Algorithm for Closed Loop System Identification Based on Data Correlation

A closed loop system identification method is developed in which estimation bias from sensor noise and external disturbances is minimized. The method, based on the instrumental variables four step algorithm (IV4), uses three steps. The first step estimates a model using cross covariance calculations between the reference input signal and the control and measured output signals. The second step employs the prefilter identification process from the IV4 process. The third and final step uses the prefilter, the instrumental variables and the reference, control and output signals to estimate the final model. The method is demonstrated on a diesel engine where an open loop model relating fueling to engine speed is sought. The identification example is complicated by the presence of nonmeasurable external torque disturbances due to vehicle accessories.Copyright