Date of Award
Master of Science
Amol D Mali
Chiu Law, Jaejin Jang
Boilers, Case-Based Reasoning, Clustering, Machine Learning, Natural Language Processing, Neural Networks
Driven by demand from both consumers and manufacturers alike, Internet of Things (IoT)
capabilities are being built into more products. Consumers want more control and access to their
devices, while manufacturers can find data gathered from IoT-capable products invaluable. In
this thesis, we use data from a growing fleet of IoT-connected boilers in the residential, lightcommercial, and medium-commercial ranges to demonstrate a framework for cluster initialization
and updating. We compare two methods of dynamically updating clusters: a sequential method
inspired by sequential K-means clustering and a cohesion-based method called DYNC. A predictive
artificial neural network system demonstrates the effectiveness of the clustering methods.
In a secondary topic, a multi-tiered case-based reasoning system (CBR) is created based on
boiler problem and repair support cases. Word embeddings are extracted from case comments and
used to predict potential solutions to problems and problem categories using user selection and
input. The primary tier uses information about actions taken involving specific parts, along with
comments fed through the word embedding model, to predict the correct next step. The secondary
tier uses only case comments to provide categories of likely symptoms and solutions. The third tier
is a pure probability fall-back model.
Rooney, Timothy Edward, "An Application of Clustering and Cluster Update Methods to Boiler Sensor Prediction and Case-Based-Reasoning to Boiler Repair" (2019). Theses and Dissertations. 2334.