This page contains a Flash digital edition of a book.
FEATURE SPONSOR ADVANCES IN REMOTE SENSING & MONITORING


TAILORED SOLUTION


The correct approach for any cost effective and data efficient system is to provide sensing capability to measure and monitor the critical asset parts with enough data sampling to give an accurate picture of the machine condition. No data redundancy is ensured with this tailored type instrument selected approach.


The M-HAS collects data real time and displays on a customised dashboard. The sensor types connected to the asset in the field provide critical data to the system and when the user connects to the system status indicators show immediate notifications and alerts.


FEEDBACK METHODS


The monitored system collects data on the server and runs sophisticated algorithms to summarise and determine the machine health statements. From acquiring and calculating these they are available to download in various formats and displayed on the machine dashboard for the client. The feedback methods can be set up to offer SMS text responses or email and either automatic or by the user logged into the site.


The system data when collected was then used to trigger detailed inspections of the turbine by way of either system automated or user generated fault reports. These typically get emailed to asset managers and operators and are easily read from laptops and portable devices.


Turner analysts provide detailed reports on findings from inspections to the customer and offer services and repair of the faulty system components.


TURBINE DATA VALUE


The sensor data when collected on the turbine and processed through the M-HAS system will be summarised into operational and conditional ‘health’ statements. A historical trail of information from these sensors will show passive or ‘normal’ requirements such as optimum servicing to be identified (for example the need for an oil change) as well as dynamic and typically fault driven changes such as gear or bearing wear due to weather related damage. In turn these planned and unplanned system statements and notifications allow operators to plan, adjust and operate the individual turbines in accordance with their condition.


Simon Lorenzo, Turner Data Analyst explained: “We have found the system when installed, adapted quickly showing a performance baseline or Health statement. This was typical with many turbine system installs and also applicable with many other industrial applications within Turner.


“The system creates data that clearly shows a maintenance cost saving due to unplanned repair and significant downtime there in. It has shown uptower and insitue repairs versus gearbox replacements and the hundreds of thousands in cost associated.’’


CONCLUSIONS


With the increasing development of real time sophisticated sensors and low cost communications methods that can be readily installed into machines and turbines, data collected and from multi sensor information makes more customised approaches possible within budgets for operational expenditure.


This can ensure that the owner operator maintains premium operational performance with minimised downtime and unplanned outages. The new ‘tools’ offered within M-HAS for feedback methods ensuring that the asset health or useful life can be maximised.


Simon Lorenzo - Data Analyst Turner Wind


Click to view more info Click to view website


= Click to view video


www.windenergynetwork.co.uk


17


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72  |  Page 73  |  Page 74  |  Page 75  |  Page 76  |  Page 77  |  Page 78  |  Page 79  |  Page 80  |  Page 81  |  Page 82