  Since the inception of Industry 4.0 in 2012, emerging technologies have
enabled the acquisition of vast amounts of data from diverse sources such as
machine tools, robust and affordable sensor systems with advanced information
models, and other sources within Smart Manufacturing Systems (SMS). As a
result, the amount of data that is available in manufacturing settings has
exploded, allowing data-hungry tools such as Artificial Intelligence (AI) and
Machine Learning (ML) to be leveraged. Time-series analytics has been
successfully applied in a variety of industries, and that success is now being
migrated to pattern recognition applications in manufacturing to support higher
quality products, zero defect manufacturing, and improved customer
satisfaction. However, the diverse landscape of manufacturing presents a
challenge for successfully solving problems in industry using time-series
pattern recognition. The resulting research gap of understanding and applying
the subject matter of time-series pattern recognition in manufacturing is a
major limiting factor for adoption in industry. The purpose of this paper is to
provide a structured perspective of the current state of time-series pattern
recognition in manufacturing with a problem-solving focus. By using an ontology
to classify and define concepts, how they are structured, their properties, the
relationships between them, and considerations when applying them, this paper
aims to provide practical and actionable guidelines for application and
recommendations for advancing time-series analytics.
