HIGH DIMENSIONAL TIME SERIES DATA MINING IN AUTOMATIC FIRE MONITORING AND AUTOMOTIVE QUALITY MANAGEMENT

Abstract

Time series data is increasingly being generated in many domains around the world. Monitoring an event using multiple variables gathered over time forms a high-dimensional time series when the number of variables is high. High-dimensional time series are being widely applied across many areas. Thus, the need to develop more efficient and effective approaches to analyze and monitor high-dimensional time series data has become more critical. For instance, within the realm of fire disaster management, the advancement of fire detection systems has garnered research interest aimed at safeguarding human lives and property against devastating fire incidents. Nonetheless, the task of monitoring indoor fires presents complexities attributed to the distinct attributes of fire sensor signals (namely, high-dimensional time series), including the presence of time-based dependencies and varied signal patterns across different types of fires, such as those from flaming, heating, and smoldering sources. In the field of automobile quality management, minimizing internal vehicle noise is crucial for enhancing both customer satisfaction and the overall quality of the vehicle. Windshield wipers are significant contributors to such noise, and defective wipers can adversely impact the driving perception of passengers. Therefore, detecting wiper defects during production can lead to an improved driving experience, enhanced vehicle and road safety, and decreased driver distraction. Currently, the process for detecting noise from windshield wipers is manual, subjective, and requires considerable time. This dissertation presents several novel time series monitoring and anomaly detection approaches in two domains: 1) fire disaster management and 2) automotive quality management. The proposed approaches effectively address the limitations of traditional and existing systems and enhance human safety while reducing human and economic losses. In the fire disaster management domain, we first propose two fire detection systems using dynamic time warping (DTW) distance measure. The first fire detection system is based on DTW and the nearest neighbor (NN) classifier (NN-DTW). The second fire detection system utilizes a support vector machine with DTW kernel function (SVM-DTWK) to improve classification accuracy by utilizing SVM capability to obtain nonlinear decision boundaries. Using the DTW distance measure, both fire detection systems retain the temporal dynamics in the sensor signals of different fire types. Additionally, the suggested systems dynamically identify the essential sensors for early fire detection through the newly developed k-out-of-P fire voting rule. This rule integrates decision-making processes from P multichannel sensor signals effectively. To validate the efficiency of these systems, a case study was conducted using a real-world fire dataset from the National Institute of Standards and Technology. Secondly, we introduce a real-time, wavelet-based fire detection algorithm that leverages the multi-resolution capability of wavelet transformation. This approach differs from traditional fire detection methods by capturing the temporal dynamics of chemical sensor signals for different fire scenarios, including flaming, heating, and smoldering fires. A novel feature selection method tailored to fire types is employed to identify optimal features for distinguishing between normal conditions and various fire situations. Subsequently, a real-time detection algorithm incorporating a multi-model framework is developed to efficiently apply these chosen features, creating multiple fire detection models adept at identifying different fire types without pre-existing knowledge. Testing with publicly available fire data indicates that our algorithm surpasses conventional methods in terms of early detection capabilities, maintaining a low rate of false alarms across all fire categories. Thirdly, we introduce an innovative fire detection system designed for monitoring a range of indoor fire types. Unlike traditional research, which tends to separate the development of fire sensing and detection algorithms, our system seamlessly integrates these phases. This integration allows for the effective real-time utilization of varied sensor signals to identify fire outbreaks at their inception. Our system collects data from multiple types of sensors, each sensitive to different fire-emitted components. This data then feeds into a similarity matching-based detection algorithm that identifies distinct pattern shapes within the sensor signals across various fire conditions, enabling early detection of fires with minimal false alarms. The efficacy of this system is demonstrated through the use of real sensor data and experimental results, underscoring the system’s ability to accurately detect fires at an early stage. Lastly, in the automotive quality management domain, we introduce an innovative automated system for detecting faults in windshield wipers. Initially, we apply a new binarization technique to transform spectrograms of the sound produced by windshield wipers, isolating noisy regions. Following this, we propose a novel matrix factorization technique, termed orthogonal binary singular value decomposition, to break down these binarized mel spectrograms into uncorrelated binary eigenimages. This process enables the extraction of significant features for identifying defective wipers. Utilizing the k-NN classifier, these features are then categorized into normal or faulty wipers. The system’s efficiency was validated using real-world datasets of windshield wiper reversal and squeal noises, demonstrating superior performance over existing methodologies. The proposed approaches excel in detecting complex temporal patterns in high-dimensional time series data, with wide applicability across healthcare, environmental monitoring, and manufacturing for tasks like vital signs monitoring, climate and pollution tracking, and machinery maintenance. Additionally, the OBSVD technique, producing binary, uncorrelated eigenimages for unique information capture, broadens its use to medical imaging for anomaly detection, security for facial recognition, quality control for defect detection, document processing, and environmental analysis via satellite imagery. This versatility highlights the research's significant potential across machine learning and signal processing, improving efficiency and accuracy in time series data analysis.