Rotating machinery - critical but costly
Rotating machinery is the backbone of almost all critical plant operations. Steam and gas turbines, motors, generators, agitators, fans, pumps and compressor systems are all rotating machines.
They are also the source of much of the plant’s operations and maintenance costs. Their behavior can be unpredictable. Reliability studies show that two-thirds of all machine failures occur randomly – with early but often undetected warning signs.
These warning signs can be picked up. Measuring the machine’s vibration levels can detect degrading performance – the greater the vibration, the greater the potential for equipment failure or unplanned shutdown.
More than monitoring
ABB Ability™ Predictive Maintenance consists of a suite of diagnostic and prognostic solutions that use process and vibration data to monitor the health of rotating equipment, identify the early signs of a potential problem, diagnose the root physical cause of the problem, analyze the probability of failure and its severity, and suggest when and how to correct or repair the problem.
The suite does this by managing two kinds of data: process data such as temperature, pressure, differential pressure, etc., which are transmitted in real time or at intervals of a few seconds; and harmonics vibration, which is typically transmitted at intervals of several minutes.
This complete condition-based predictive maintenance solution is modular by design, enabling customers to install either the full suite or specific modules to match their requirements.
Data from the modules are seamlessly linked to the ABB AbilityTM Symphony® Plus high-performance HMI, S+ Operations, where they are transformed into meaningful information and presented in intuitive user-specific desktop displays to enable fast and correct decision-making.
Correct information about the health status of the equipment not only reduces machine breakdowns and plant downtime, it optimizes production by allowing maintenance to be performed during the least profitable times of the day.
20% reduction in maintenance work
As part of a collaborative operations project between ABB and a global energy company, the predictive maintenance solution was installed in a small 5.5 MW hydropower plant in Europe, whose three turbines were built in 1927. The solution’s initial purpose was to monitor plant health for a year to determine the difference between actual and potential performance.
During this time, the solution detected cavitation in a pump impeller, which was unknown to staff and detectable only by monitoring. Having discovered the fault, the solution performed a root cause analysis and forecast the time left before the condition became critical. This enabled the plant to adjust its maintenance schedule and repair the fault at the optimal time.
Overall, the solution improved the baseline performance of plant equipment, made maintenance more efficient and reduced maintenance working hours by 20 percent.
$1.5 million per year savings in energy costs
An ABB performance monitoring system, part of a larger predictive maintenance solution and collaborative operations project, was installed in a combined cycle power plant in Europe, comprising two 380 MW units.
Each unit has an air condenser, which requires a large fan to extract air. In the summer, the fans were often running at maximum speed round the clock. Performance monitoring detected that this was unnecessary, and that excessive dirt in the fan was the reason for the fans overworking.
Instead of cleaning the fans just once a year in winter, the maintenance schedule was changed to twice a year, thanks to the performance monitoring results. This new maintenance policy significantly reduced the fans’ energy consumption and carbon dioxide emissions by $1.5 million and 8,000 tons per year respectively.
Recovering 10 MW of turbine output
A gas turbine with a rated output of 250 MW at a combined cycle plant in Europe was underperforming by 10 MW.
Working collaboratively with the customer, ABB installed its Symphony Plus turbine monitoring module to monitor performance and diagnose the reason for the 10 MW loss in output.
The solution pinpointed the source of the loss to a compressor. After repair, the turbine regained its maximum output of 250 MW, an improvement worth about $1.5 million annually in revenues. l