Applying Sound-Based Analysis at Porsche Production: Towards Predictive Maintenance of Production Machines Using Deep Learning and Internet-of-Things Technology

Mauer, Matthias Auf der; Behrens, Tristan; Derakhshanmanesh, Mahdi; Hansen, Christopher L.; Muderack, Stefan

doi:10.1007/978-3-319-95273-4_5

Cited by 21 publications

(13 citation statements)

References 6 publications

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“…Mechanical systems wear down, leading to different operating states that a diagnostic tool must be able to detect in order to time preventative maintenance properly. To address this need, a "sound detective" was created to classify the different operating states of various machines [34].…”

Section: Vehicle Conditionmentioning

confidence: 99%

Surveying Off-Board and Extravehicular Monitoring and Progress Towards Pervasive Diagnostics

Siegel¹,

Coda²,

Terwilliger³

2021

SAE Intl. J CAV

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We survey the state-of-the-art in offboard diagnostics for vehicles, their occupants, and environments, with particular focus on vibroacoustic approaches. We identify promising application areas including data-driven management for shared mobility and automated fleets, usage-based insurance, and vehicle, occupant, and environmental state and condition monitoring. We close by exploring the particular application of vibroacoustic monitoring to vehicle diagnostics and prognostics and propose the introduction of automated vehicle-and context-specific model selection as a means of improving algorithm performance, e.g. to enable smartphone-resident diagnostics. The described approach may serve as the first step in developing "universal diagnostics," with applicability extending beyond the automotive domain. Motivating the Need for Vehicle DiagnosticsThe automotive world is changing, and there is increasing concern about vehicles' environmental impact, particularly those with internal combustion engines. As a result, there is a growing prevalence of efficiency-improving systems within vehicles. One of the biggest contributors to lifetime efficiency is the availability of in-vehicle diagnostic systems, as these may report faults early and precisely to motivate owners and operators to seek out preventative or restorative maintenance, enhancing safety and reducing operating costs.At the same time, mobility culture is evolving, transitioning from the need to own a car towards the concept of mobilityas-a-service. Today, vehicle sales are slowing despite continued high mobility demand: The average vehicle age and lifetime miles travelled are increasing, particularly in developing countries [1,2], and shared mobility services, car rentals, and "robotaxis" are emerging. Increased utilization and novel use cases require enhanced fleet data generation and management capabilities. Key to enhanced fleet management is the concept of automotive diagnostics, i.e. the inference of a vehicle's condition based on observed symptoms indicating a technical state [3]. Diagnostics are important for monitoring vehicle, environment, and occupant status (e.g. component wear, road conditions, or driver alertness).Historically, these diagnostics draw upon in-situ sensors and computation to develop "On Board Diagnostics." On-Board Diagnostics (OBD)On-Board Diagnostic (OBD-II) systems present on vehicles sold since 1996 [4] are an automated control system utilizing distributed sensing across a vehicle's embedded systems as a technical solution for measuring vehicle operational parameters and detecting, reporting, and responding to faults.

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Section: Vehicle Conditionmentioning

confidence: 99%

Surveying Off-Board and Extravehicular Monitoring and Progress Towards Pervasive Diagnostics

Siegel¹,

Coda²,

Terwilliger³

2021

SAE Intl. J CAV

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“…A UDIO classification is a fundamental building block of many smart Internet of Things (IoT) applications such as predictive maintenance [1]- [3], surveillance [4], [5], and ecosystem monitoring [6], [7]. Smart sensors driven by microcontroller units (MCUs) are at the core of these applications.…”

Section: Introductionmentioning

confidence: 99%

Pruning vs XNOR-Net: A Comprehensive Study of Deep Learning for Audio Classification on Edge-Devices

2022

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Deep learning has celebrated resounding successes in many application areas of relevance to the Internet of Things (IoT), such as computer vision and machine listening. These technologies must ultimately be brought directly to the edge to fully harness the power of deep leaning for the IoT. The obvious challenge is that deep learning techniques can only be implemented on strictly resource-constrained edge devices if the models are radically downsized. This task relies on different model compression techniques, such as network pruning, quantization, and the recent advancement of XNOR-Net. This study examines the suitability of these techniques for audio classification on microcontrollers. We present an application of XNOR-Net for end-to-end raw audio classification and a comprehensive empirical study comparing this approach with pruning-and-quantization methods. We show that raw audio classification with XNOR yields comparable performance to regular full precision networks for small numbers of classes while reducing memory requirements 32-fold and computation requirements 58-fold. However, as the number of classes increases significantly, performance degrades, and pruning-and-quantization based compression techniques take over as the preferred technique being able to satisfy the same space constraints but requiring approximately 8x more computation. We show that these insights are consistent between raw audio classification and image classification using standard benchmark sets. To the best of our knowledge, this is the first study to apply XNOR to end-to-end audio classification and evaluate it in the context of alternative techniques. All codes are publicly available on GitHub.

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“…Intelligent sound recognition is receiving strong interest in a growing number of application areas, from technical safety [1,2,3], surveillance and urban monitoring [4] to environmental applications [5,6].…”

Section: Introductionmentioning

confidence: 99%

Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices

Mohaimenuzzaman,

Bergmeir,

West

et al. 2021

Preprint

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We introduce the first ever pipeline for sound classification using Deep Learning in extremely resource-constrained (battery powered, low memory and CPU speed) microcontrollers (MCUs).• Firstly, we present a CNN architecture (ACDNet) that achieves stateof-the-art classification accuracy on ESC-10 and ESC-50 benchmark datasets.• Secondly, we compress ACDNet using our proposed hybrid structured compression technique to obtain Micro-ACDNet that is 97.22% smaller in size and requires 97.28% less FLOPs yet produces close to state-ofthe-art performance.• Finally, after 8-bit quantization, we deploy Micro-ACDNet on a standard off-the shelf MCU.

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Applying Sound-Based Analysis at Porsche Production: Towards Predictive Maintenance of Production Machines Using Deep Learning and Internet-of-Things Technology

Cited by 21 publications

References 6 publications

Surveying Off-Board and Extravehicular Monitoring and Progress Towards Pervasive Diagnostics

Surveying Off-Board and Extravehicular Monitoring and Progress Towards Pervasive Diagnostics

Pruning vs XNOR-Net: A Comprehensive Study of Deep Learning for Audio Classification on Edge-Devices

Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices

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