Machine Hearing for Industrial Acoustic Monitoring using Cochleagram and Spiking Neural Network

Dora, Shirin; Martínez-García, Miguel; Bhattacharyaand, Saugat

doi:10.1109/aim52237.2022.9863412

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…The cochleagram technique is known for its superior performance in robust feature extraction (Y. Zhang et al, 2022). By adjusting the central frequency, the filter bank captures information from various frequency bands, facilitating the extraction of harmonic features (Abdul et al, 2020).…”

Section: Filter Bank 3: Gammatonementioning

confidence: 99%

Exploring Filter Banks and Spike Interval Statistics of Level-Crossing ADCs for Fault Diagnosis of Rolling Element Bearings

Kumar,

Strömbergsson,

Marklund

et al. 2023

PHM_CONF

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Nowadays, lots of data are generated in industries using vibration sensors to evaluate the equipment’s working condition and identify faults. A significant challenge is that only a small fraction of data can be transmitted for intelligent fault diagnosis and storage. The edge processing capacity is often insufficient for advanced analysis due to time and resource constraints. The neuromorphic signal encoding scheme efficiently reduces the data rate by encoding relevant signal changes into spike trains while discarding redundant information and noise, enabling energy-efficient neuromorphic processing. Due to the presence of dominant operational features and noise in the original measurements, signal pre-processing is required to extract the relevant features before spike coding and processing. The work investigates the effects of different filter banks (pre-processing methods) on the spike encodings for vibration measurements from bearings. This also includes bearing fault features diagnosis based on statistical analysis of generated spikes. The comparative analysis is made for benchmarking different signal pre-processing methods (e.g., envelope, empirical mode decomposition (EMD), and gammatone filter) on bearing vibration datasets. An event-triggered scheme, i.e., Level-crossing analog-to-digital converters (LC-ADCs) is applied to encode the vibration measurement to spikes. Inter-spike intervals (ISIs) statistics are analysed for fault diagnosis of bearings. The results obtained for CWRU bearing databases indicate a possible fault detection and diagnosis with significant data rate reduction and an opportunity for improved computational efficiency. With the developed approach, the envelope filter is found to be the most efficient of all. This work enables a new approach to improve the energy efficiency of condition monitoring systems and further sets a new course of research development in this area using neuromorphic technologies.

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Section: Filter Bank 3: Gammatonementioning

confidence: 99%

Exploring Filter Banks and Spike Interval Statistics of Level-Crossing ADCs for Fault Diagnosis of Rolling Element Bearings

Kumar,

Strömbergsson,

Marklund

et al. 2023

PHM_CONF

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Optimal Feature Selection via Bayesian Optimisation for Acoustic Condition Monitoring

Zhang,

García,

Wan

2023

2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)

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Acoustic-Based Machine Condition Monitoring—Methods and Challenges

Jombo

Zhang

2023

Eng

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The traditional means of monitoring the health of industrial systems involves the use of vibration and performance monitoring techniques amongst others. In these approaches, contact-type sensors, such as accelerometer, proximity probe, pressure transducer and temperature transducer, are installed on the machine to monitor its operational health parameters. However, these methods fall short when additional sensors cannot be installed on the machine due to cost, space constraint or sensor reliability concerns. On the other hand, the use of acoustic-based monitoring technique provides an improved alternative, as acoustic sensors (e.g., microphones) can be implemented quickly and cheaply in various scenarios and do not require physical contact with the machine. The collected acoustic signals contain relevant operating health information about the machine; yet they can be sensitive to background noise and changes in machine operating condition. These challenges are being addressed from the industrial applicability perspective for acoustic-based machine condition monitoring. This paper presents the development in methodology for acoustic-based fault diagnostic techniques and highlights the challenges encountered when analyzing sound for machine condition monitoring.

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Machine Hearing for Industrial Acoustic Monitoring using Cochleagram and Spiking Neural Network

Cited by 3 publications

References 22 publications

Exploring Filter Banks and Spike Interval Statistics of Level-Crossing ADCs for Fault Diagnosis of Rolling Element Bearings

Exploring Filter Banks and Spike Interval Statistics of Level-Crossing ADCs for Fault Diagnosis of Rolling Element Bearings

Optimal Feature Selection via Bayesian Optimisation for Acoustic Condition Monitoring

Acoustic-Based Machine Condition Monitoring—Methods and Challenges

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