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AI-based acoustic condition monitoring of industrial machinery

Total Cost €


EC-Contrib. €






 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.

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

Project "Motorlisten" data sheet

The following table provides information about the project.


Organization address
address: RIGAKADE 10
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
 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


Take a look of project's partnership.

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


 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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