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Motorlisten

AI-based acoustic condition monitoring of industrial machinery

Total Cost €

0

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.

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

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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