Frontiers in Data-driven Learning via Probabilistic Finite State Automata
Open Access
- Author:
- Bhattacharya, Chandrachur
- Graduate Program:
- Mechanical Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- February 23, 2022
- Committee Members:
- Minghui Zhu, Outside Unit Member
Shashi Phoha, Outside Field Member
Daniel Haworth, Major Field Member
Achintya Mukhopadhyay, Special Member
Asok Ray, Chair & Dissertation Advisor
Daniel Haworth, Professor in Charge/Director of Graduate Studies - Keywords:
- Data-driven
Machine Learning
Time-series analysis
Combustion Instablity
Thermoacoustic Instability
Pattern Recognition
Reduced Order Modeling
Rijke tube
Probabilisitc Finite State Automata
Symbolic Time Series Analysis
Combustion dynamics
Combustion
Hidden Markov Model
LSTM
Projection PFSA
Chaos
Natural Circulation Loop
Crack detection
Online Learning
Transfer Learning
DDDAS
PFSA
STSA - Abstract:
- Anomaly detection is an essential step in the task of automating complex processes, allowing the control algorithm to identify any undesirable operation and take preventive or corrective actions as needed. Even under normal conditions, dynamical systems may have several regimes of operation, and identification of the current operational regime is essential for effective monitoring and control of the process. The most common form of information obtained from these processes is time-series data, which are typically from sensors of various kinds distributed over the system. Thus, many data-driven methods exist for learning how to identify (possible) anomalies from time-series data and to classify the time-series into one of the several classes, including anomalous and normal ones. This dissertation presents one such data-driven time-series analysis method that combines the concepts of Symbolic Time Series Analysis (STSA) and Probabilistic Finite State Automata (PFSA), leading to the concept of $D$-Markov models. This method has several distinct advantages over several state-of-the-art methods, such as; fast execution due to simple algebraic construction, high detection and classification accuracy, and, feature interpretability. Originally developed about two decades ago, the traditional formulation had several shortcomings, which have been largely improved upon in the work reported in this dissertation; including development of a more robust mathematical methodology and augmentation of the formulation to retain more information from the original signal, which allows superior classification performance. This improved PFSA methodology is demonstrated on several real-life engineering problems, as well as some numerical examples, to demonstrate the efficacy of the method in identifying exceptionally complex (e.g., chaotic) signals. The engineering problems include identifying thermo-acoustic instability in combustion systems; detection of crack appearance in structural members, and identification of operational regimes in natural circulation loops. Further, using numerical examples generated from chaotic systems, the PFSA methods are demonstrated to have good classification accuracy and phase change detection capabilities. This dissertation also introduces two novel PFSA-based algorithms for transfer learning and online pattern learning. Transfer learning is a method to learn from one data-set and applying the learnt knowledge to a different, but somewhat similar, data-set without any major re-learning. With many possible applications, the development of a PFSA-based methodology which is computationally faster than the traditional deep learning methods, is an interesting alternative. In the context of online learning, the PFSA-based algorithm is capable of identifying new, previously unseen patterns in real-time while intelligently learning to group the newly observed patterns, thereby expanding its library of classes. All the improvements and new approaches, developed in this dissertation, hope to bring PFSA-based methods to the more mainstream literature and gain widespread usage.