MOSYCOUSIS

Intelligent Monitoring System based on Acoustic Emissions Sensing for Plant Condition Monitoring and Preventative Maintenance

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 Obiettivo del progetto (Objective)

'Unexpected failure in an industrial production chain does not only involve the costs of failed parts replacement and the associated man-hour labour, but downtime costs have also have to be considered. To keep a machine functioning well it is a must to have good predictive maintenance, as it helps to reduce operating risk, avoids plant failures, provides reliable equipment, reduces operating costs, eliminates defects in operating plant and maximises production. Acoustic Emission (AE) is a phenomenon of transient elastic wave generation in materials under stress. When the material is subjected to stress at a certain level, a rapid release of strain energy takes place in the form of elastic wave which can be detected by transducers placed on it. Plastic deformation and growth of cracks are among the main sources of AE in metals. Though AE can came form any system under movement, the main source is doubtlessly from rotating machinery. Sources of AE in rotating machinery include impacting, cyclic fatigue cracks, friction, turbulence, material loss, cavitation, leakage, etc. In most cases the SMEs machine owner would be satisfied with a simple affordable device that is able to warn them from critical equipment failure.

Recent developments in sensing technology, microprocessors, and miniaturised radio transceivers has enabled a new generation of Wireless Sensors Networks. The future of these sensors is to have an ubiquitous sensing nodes that will autonomously report on operating conditions, and that this data will be used to facilitate structural health monitoring, embedded test & evaluation, and condition based maintenance of critical industrial rotating machinery without the use of expensive cabling. In addition, in order to provide sensing networks which are truly autonomous, chemical batteries must be eliminated from the sensor and some kind of energy harvesting has to be foreseen. Piezoelectric materials have demonstrated their ability to convert vibration energy from vibrating machinery and rotating structures into electrical energy for powering a wireless sensing node. Hence, an acoustic emission self-powered wireless sensor is one of the main objectives to be achieved in this project. The sensor will measure using frequency as opposed to time which is an advancement from the state of the art.'

Introduzione (Teaser)

Unexpected machine failure can cost small and medium-sized enterprises (SMEs) a fortune. Novel, inexpensive non-destructive testing technology developed with EU support will enhance monitoring to enable intervention before catastrophic failure.

Descrizione progetto (Article)

From replacing broken parts to lost productivity due to downtime to the cost of additional labour not to mention potential issues with dissatisfied customers, SMEs need protection against machinery loss. The methods currently employed by large enterprises are cost-prohibitive for SMEs.

The EU-funded project http://mosycousis.ctm.com.es/ (MOSYCOUSIS) created a smart acoustic emission (AE) sensor incorporating on-board data acquisition, conditioning and processing systems for real-time monitoring and preventive maintenance.

AE is the sudden release of strain energy in the form of transient elastic waves from a material under stress. Particularly in rotating machinery in industrial plants, AE can occur during impacting, cyclic fatigue cracks, friction, turbulence, material loss, cavitation or leakage. Researchers developed an intelligent monitoring system through self-powered wireless sensor networks using microprocessors and miniaturised radio transceivers.

Considerable focus was placed on fault condition characterisation using data generated from simulations, laboratory tests and industrial machinery. The resulting database contains comprehensive information generated by testing mechanical components and metallographic samples under static and dynamic conditions.

Sensor hardware includes an AE conditioning module that can manage up to three channels for analysis by the fault detection algorithm and the power supply module that incorporates three possible energy harvesting modes that utilise waste energy in addition to a conventional supply.

Software development consists of specific algorithms exploiting signal processing and AE theory to correlate AEs to faults in the machinery, to perform machine prognosis and to calculate life expectancy.

The cost-effective MOSYCOUSIS condition-based predictive system will improve production quality and workforce safety while reducing production time. This along with reductions in workforce and machine downtime from failures will considerably increase profitability through lower overheads. The need for this system is particularly felt during this period of economic recession. Successful outcomes will thus increase the competitiveness of participating SMEs as well as end users with important implications for the EU economy.