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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.

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

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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