Nayeff Najjar

Optimal Sensor Selection and Fusion for Heat Exchanger Fouling Diagnosis in Aerospace Systems

Heat exchangers are critical components of the environmental control system (ECS) of an aircraft. The ECS regulates temperature, pressure, and humidity of the cabin air. Fouling of the heat exchangers in an ECS may occur due to the deposition of external substances (e.g., debris) on the fins that obstruct the air flow, which increases the pressure drop across the heat exchanger and degrades its efficiency. Fouling is a critical issue, because it necessitates time consuming, periodic, and expensive maintenance. In this regard, this paper presents a two step process for fouling diagnosis of the heat exchanger: optimal sensor set selection that contains the most relevant information for fault classification and robust data analysis and sensor fusion in the presence of various uncertainties for the inference of fouling severity via different machine learning tools. This process of heat exchanger fouling diagnosis is implemented and tested on the data generated from an experimentally validated high-fidelity Simulink model of the ECS provided by an industry partner.

Human activity recognition using LZW-Coded Probabilistic Finite State Automata

Human activity recognition has become an increasingly important field of research with many practical applications related to health care and leisure activities. The accessibility of inexpensive portable sensors, such as accelerometers, allows for a widespread use of this technology for both commercial and personal activity recognition. This paper develops a novel feature extraction approach to human activity recognition through the development of the Lempel-Ziv-Welch Coded Probabilistic Finite State Automata (LZW-Coded PFSA) to classify activities such as walking, jumping, running, waist rotations, and shoulder rotations. The PFSA reveal the underlying architecture of a given activity and classify it without making any a priori assumptions by inferring patterns from the sensor measurements. LZW-Coded PFSA select the optimal variable length state from the time-series data and compress it into class-separable state transition matrices π. This algorithm is robust to subject biases and is shown to be effective with a correct classification rate of 95.63%.

Better-than-the-best fusion algorithm with application in human activity recognition

This paper introduces the Better-than-the-Best Fusion (BB-Fus) algorithm. The BB-Fus algorithm is a simple and effective information fusion algorithm that combines the information from different sources (be it sensors, features or classifiers) to improve the Correct Classification Rate (CCR). It can be observed that in most classification problems, different sensors or features might have different classification accuracies in separating different classes. Therefore, this paper constructs an optimal decision tree that isolates one class at a time with the best sensor to separate that particular class. The paper shows that the decision tree improves the overall CCR as compared to the use of any single sensor or feature for any 3-class classification problem. The efficiency of the BB-Fus algorithm is validated on the Opportunity data set to solve the human activity recognition problem where a set of 56 sensors (including a localization system, accelerometers, inertial measurement units and magnetic sensors mounted on various body parts; besides, accelerometers and gyroscopes mounted on different objects) are used. The CCR resulting from the BB-Fus algorithm is 96% while the best sensor achieved 94% CCR.