Cost-effective vibration analysis through data-backed pipeline optimisation

TL;DR

This paper presents a data-driven approach to optimize vibration analysis pipelines for IoT devices, balancing classification accuracy with reduced sampling rates and shorter observation windows to lower costs and computational demands.

Contribution

It introduces a methodology for optimizing and justifying pipeline complexity, and evaluates the impact of sampling rate and window length on classification performance.

Findings

Lowering sampling rate from 50 kHz to 1 kHz causes a significant performance drop.

Shortening observation windows from 5 seconds to 0.1 seconds significantly reduces accuracy.

Optimizations enable more cost-effective and efficient IoT vibration analysis systems.

Abstract

Vibration analysis is an active area of research, aimed, among other targets, at an accurate classification of machinery failure modes. This often leads to complex and convoluted signal processing pipeline designs, which are computationally demanding and cannot be deployed in the Edge devices. In the current work, we address this issue by proposing a data-driven methodology that allows optimising and justifying the complexity of the signal processing pipelines. Additionally, aiming to make IoT vibration analysis systems more cost- and computationally effective, on the example of MAFAULDA vibration dataset, we assess the changes in the failure classification performance at low sampling rates as well as short observation time windows. We find out that a decrease of the sampling rate from 50 kHz to 1 kHz leads to a statistically significant classification performance drop. A statistically significant decrease is also observed for the 0.1 second time windows compared to the 5-second ones. However, the effect sizes are small to medium, suggesting that in certain settings lower sampling rates and shorter observation windows can be used. The proposed optimisation approach, as well as statistically supported findings of the study, allow a more efficient design of IoT vibration analysis systems, both in terms of complexity and costs, bringing us one step closer to the IoT/Edge-based vibration analysis.