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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.

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

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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