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Motorlisten

AI-based acoustic condition monitoring of industrial machinery

Total Cost €

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EC-Contrib. €

0

Partnership

0

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 Motorlisten project word cloud

Explore the words cloud of the Motorlisten project. It provides you a very rough idea of what is the project "Motorlisten" about.

optimize    untreated    lower    objects    global    larger    minimize    asset    initial    wind    turbines    human    machine    grumble    auditory    3bn    noises    break    300    big    faults    trl    plants    internet    manufacturers    customer    commercial    machines    alike    staff    heat    energy    picks    invented    consumption    lifetime    water    combining    grumbles    sensor    industry    fault    ears    left    emit    happen    signals    expert    accuracy    invasive    industries    talk    strategy    facility    physical    mechanical    learning    unusual    quantify    algorithm    detect    infinitely    unscheduled    valves    motor    data    market    predictive    companies    gearboxes    machinery    worth    ai    smart    longer    downtime    exactly    sounds    things    attractive    emulates    feasibility    maintenance    onewatt    pumps    unplanned    predict    scalable    hearing    industrial    maintainance    assets    mechanic    utilities    hour    acoustic    99    warning    tell    uses    markets   

Project "Motorlisten" data sheet

The following table provides information about the project.

Coordinator
ONEWATT SOLUTIONS BV 

Organization address
address: RIGAKADE 10
city: AMSTERDAM
postcode: 1013 BC
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Netherlands [NL]
 Project website http://onewatt.eu
 Total cost 71˙429 €
 EC max contribution 50˙000 € (70%)
 Programme 1. H2020-EU.3. (PRIORITY 'Societal challenges)
2. H2020-EU.2.3. (INDUSTRIAL LEADERSHIP - Innovation In SMEs)
3. H2020-EU.2.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies)
 Code Call H2020-SMEInst-2018-2020-1
 Funding Scheme SME-1
 Starting year 2018
 Duration (year-month-day) from 2018-08-01   to  2019-01-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    ONEWATT SOLUTIONS BV NL (AMSTERDAM) coordinator 50˙000.00

Map

 Project objective

'Before the break, mechanical objects emit unusual noises - machines talk and grumble. These grumbles are warning signals that a fault is developing, which if left untreated can lead to motor failure and unscheduled downtime in the facility. At costs of up to €300,000 per hour, unplanned downtime is a very big problem for industrial plants and utilities alike. OneWatt has invented a non-invasive predictive maintenance system, combining an auditory sensor ('EARS'), which picks up a machine's grumbles, with an AI machine-learning algorithm. The system, developed to TRL 7, can detect and predict physical faults in machinery - and can tell maintenance staff not only that a fault is developing but exactly how, where and when the fault will happen. The system emulates an expert mechanic, who can identify faults just by hearing motor sounds, but because it uses AI and an infinitely larger data set than a human can experience, it is much more reliable than any human could be - and scalable. This will optimize maintainance work and minimize downtime, a big priority for industrial companies and utilities, who will be the initial customer targets. The potential market is global, worth an estimated € 3bn. OneWatt's system will help companies implement a much more targeted, cost-effective 'smart maintenance' strategy and become part of Industry 4.0 technology and the 'Industrial Internet of Things'. OneWatt's system will also be very attractive for other industries that have assets that emit acoustic signals, such as gearboxes or valves. Future target markets will include wind turbines, heat pumps and water distribution equipment. The objectives of the Phase 1 feasibility study are (i) to establish the parameters required to reach 99.99% accuracy; quantify targets and establish methodologies to achieve longer asset lifetime and lower energy consumption and (ii) to analyse the commercial potential of the technology among industrial manufacturers and utilities.'

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The information about "MOTORLISTEN" are provided by the European Opendata Portal: CORDIS opendata.

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