Online condition monitoring keeps plant running

Unplanned downtime on high-speed paper machines can cut into a mill's profitability through both lost production and the diversion of engineering resources to get a machine back up and running.

Unplanned downtime on high-speed paper machines can cut into a mill's profitability through both lost production and the diversion of engineering resources to get a machine back up and running.

Since installing SKF Reliability Systems condition monitoring technology and equipment, St Regis, one of the UK's leading paper and board manufacturers, has significantly reduced unscheduled stoppages at its Wansbrough Mill - a converting mill with an annual output of 135kt that recycles paper and card.

The mill uses 100% recycled fibres on two machines: paper machine one (PM1), a single-wire machine glazing unit which produces recycled envelope papers; and paper machine 5 (PM5), a twin-wire Fourdrinier producing coreboard for packaging.

As with any modern plant, in order to be competitive, the mill must maintain high levels of productivity.

Planned maintenance is scheduled at the plant to take place on a monthly basis.

However, before installing an SKF online condition monitoring surveillance system on its PM5 machine, unplanned stoppages were commonplace.

The mill's previous condition monitoring regime relied on operators using their experience to identify problems, a process that usually resulted in problems being identified at too late a stage; when an immediate unplanned shutdown was often the only suitable solution.

With this in mind, St Regis approached SKF Reliability Systems to review its condition monitoring and maintenance strategy with the goal of maximising the efficiency of the mill and minimising downtime.

On examining the two paper machines, SKF engineers decided to apply two different strategies.

PM5 now has a full SKF online condition monitoring surveillance system incorporating 18 SKF local monitoring units (LMUs), which log and process machine data using 32 channels per LMU and 564 fixed sensors (accelerometers).

60 additional fixed sensors are terminated to switchboxes outside the machine guards.

The sensors convert machine vibrations into a velocity signal, which takes the form of a sinusoidal waveform.

Different waveforms are indicative of specific problems with bearing wear and fatigue, machine imbalance, misalignment and mechanical looseness.

The sensor measurements are passed from the networked LMUs to a standard office PC running SKF Reliability Systems proprietary Prism4 software - a Windows-based data management and analysis tool, which analyses the incoming signals and converts them into an easy-to-read, traffic light display for each point.

Developing problems can be identified early enough to be dealt with during the next monthly planned maintenance shutdown.

PM1 did not require a complete automated system; the amount of unplanned downtime experienced did not warrant a full online condition monitoring system.

It was therefore decided to monitor the machine's glazing roll on a weekly basis with an SKF Microlog portable data collector/analyser.

This collects, stores, trends and analyses vibration and process data before processing within SKF Prism4 in the same way as those from PM5.

All the ancillary plant associated with PM1 and PM5, such as pumps, motors, fans and gearboxes, are also monitored - using SKF Reliability Systems Marlin process data collectors/analysers.

All the collected data are uploaded to a host PC for the review by the mill's engineering team.

Any worrying data are followed up with the SKF Microlog, which provides a more detailed analysis.

As a matter of course, the daily Marlin routes are also covered on a weekly basis using a Microlog.

As a result of installing the SKF online condition monitoring system and implementing the other condition monitoring and maintenance regimes, the mill has significantly reduced unplanned downtime.

On PM5 alone, almost all unplanned mechanical downtime has been eliminated.

As well as improving unplanned downtime, the automatic monitoring system has also helped to improve health and safety at the plant.

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