Objetivo
"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."
Ámbito científico
- social scienceseconomics and businesseconomicsproduction economicsproductivity
- engineering and technologyenvironmental engineeringenergy and fuelsrenewable energywind power
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- social scienceseconomics and businessbusiness and managementemployment
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programa(s)
Convocatoria de propuestas
Consulte otros proyectos de esta convocatoriaConvocatoria de subcontratación
H2020-SMEInst-2018-2020-1
Régimen de financiación
SME-1 - SME instrument phase 1Coordinador
1013 BC AMSTERDAM
Países Bajos
Organización definida por ella misma como pequeña y mediana empresa (pyme) en el momento de la firma del acuerdo de subvención.