Detecting Drill Failure in the Small Short-sound Drill Dataset

TL;DR

This paper presents a CNN-LSTM based approach with data augmentation and attention mechanisms to detect drill failures from sound data, achieving over 92% accuracy in a challenging small dataset.

Contribution

It introduces a novel combination of CNN, LSTM, and attention for drill failure detection using limited and complex sound data, with effective data augmentation.

Findings

Achieved 92.35% accuracy in drill failure detection.

Enhanced detection performance with CNN-LSTM and attention mechanisms.

Demonstrated effectiveness on a small, imbalanced dataset.

Abstract

Monitoring the conditions of machines is vital in the manufacturing industry. Early detection of faulty components in machines for stopping and repairing the failed components can minimize the downtime of the machine. This article presents an approach to detect the failure occurring in drill machines based on drill sounds from Valmet AB. The drill dataset includes three classes: anomalous sounds, normal sounds, and irrelevant sounds, which are also labeled as "Broken", "Normal", and "Other", respectively. Detecting drill failure effectively remains a challenge due to the following reasons. The waveform of drill sound is complex and short for detection. Additionally, in realistic soundscapes, there are sounds and noise in the context at the same time. Moreover, the balanced dataset is small to apply state-of-the-art deep learning techniques. To overcome these aforementioned difficulties, we augmented sounds to increase the number of sounds in the dataset. We then proposed a convolutional neural network (CNN) combined with a long short-term memory (LSTM) to extract features from log-Mel spectrograms and learn global high-level feature representation for the classification of three classes. A leaky rectified linear unit (Leaky ReLU) was utilized as the activation function for our proposed CNN instead of the rectified linear unit (ReLU). Moreover, we deployed an attention mechanism at the frame level after the LSTM layer to learn long-term global feature representations. As a result, the proposed method reached an overall accuracy of 92.35% for the drill failure detection system.