Pattern Identifications in Transformed Acoustic Signals Using Classification Models
Year: 2024
Type: article
Abstract: Pattern identifications in signals is necessary to discern variations from approved normal values in different scenarios. With machine learning algorithms, it is possible to use hybrid methods of pattern identifications such as feature extractions followed by classifications and/or clustering. This paper presents a pattern identification approach of acoustic signals using their transformations as inputs to classification algorithms. The analysis is carried out on two transformed versions of acoustic emission (AE) hits such as log transformations of peak hits and binary sequencing based on threshold crossing. A comparative analysis using custom data loss metrics is made to determine which inputs provide the best information in predictive methods for identifying commonly occurring patterns while acknowledging significant data loss. The methodology is conducted on transformed versions of a public dataset and the results show that patterns can be discerned to above 90% accuracy with the transformed datasets. The experimental results yield that actual source signals need not be utilized depending on which transformations suit the practical application.
Source: Procedia CIRP
Institution: Technische Hochschule Ingolstadt
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404789660