Although many companies still rely solely on an API 670 protection system to assess the health and performance of their turbo generation machinery, the high cost of production breakdowns can justify an investment in technology that can predict future deterioration and the cause of current or pending faults.

Christopher Walczuk, Emerson Process Management, USA

Today’s advanced technology creates a need for decision-making information about the operating condition of critical rotating and mechanical equipment – rather than just a ‘trip’ signal after significant internal damage has already occurred. Companies can put their productivity at risk by relying solely on “protection” systems for their critical turbo machinery. Vital though these are, they are only part of a bigger solution.

A complete strategy for protecting turbo machinery extends from protection to prediction and performance monitoring technologies irrespective of the scenario. These events include:

  • Unpredictable events: malfunctions that happen suddenly and without warning when, for instance, a metallurgical imperfection or slug, or water from the boiler, causes a blade to snap. When this occurs, an instant decision to ‘trip’ must be made, and integrated with process control to orchestrate a shutdown of the machine, area or plant. In addition, machinery data gathered before and during the ‘trip’ will aid the assessment of what happened.
  • Predictable events: machine malfunctions that are detected and tracked months in advance of a planned outage. Maintenance planners use this information to identify the area of the fault and its type, to gauge its severity, to order parts and to plan the outage. When malfunctions in this category are monitored, the business decision can be made to either continue running the machine and risk damage, or to determine the most convenient time for scheduling the outage. In parallel, the protection system is monitoring for any sudden change to avoid catastrophic failure.
  • Controllable events: scenarios that give the largest return on investment for monitoring capital outlay, and provide the best opportunity to optimize process and performance. For example, on an unusually cold day, the operator may ramp up the turbine and receive an oil whirl vibration alert from the predictive vibration monitoring system and simultaneously see a low temperature alarm from the process control system on the same bearing. This is a controllable scenario and the operator knows exactly what to do. Reducing the RPM of the turbine will immediately stop an oil whirl from damaging the bearing. Solving the low oil temperature problem will remove the problem of oil whirl when the turbine is brought back on-line. In controllable scenarios, an operator has information on both the health of the machine and the process status and can avoid problems that would otherwise lead to machine degradation.

Prediction monitoring

Predictive maintenance of rotating assets is best practised using information gathered through vibration monitoring. This data can signal an impending possible problem allowing analysts to make a judgement on when a failure might be expected. Based on their prediction, immediate repairs may be considered necessary to avoid the failure. But it may be possible to delay repairs until a scheduled plant shutdown – or to dispense with them entirely. Ultimately, this technology helps the plant and maintenance managers make business decisions about what to do – and when and how to do it. The result is generally a far less expensive proposition than reacting after something breaks.

Yet a Deloitte & Touche study found that more than 50 per cent of industry maintenance man-hours are spent fixing equipment following a failure, while less than 18 per cent of those hours are spent determining when equipment might fail and planning accordingly. These numbers will improve as more maintenance departments implement predictive maintenance programmes based on on-line vibration monitoring of key machines.

The “most critical” category usually only covers about 5 per cent of rotating assets, but these few machines represent an easy target for a complete online monitoring solution – with huge financial returns on a single “find” with a controllable outcome. This category includes turbo machinery.

Continuous, on-line monitoring of turbo machinery represents a major technological advance from systems that provide only periodic snapshots. Some critical situations can only be averted if a stream of data about the current condition of the equipment is available. Fortunately, it is now possible to continuously obtain information about the health of a whole range of gas or steam turbines, generators, compressors, fans, motors and pumps. Essential equipment can be monitored automatically for changing vibration patterns and rising temperatures – sure signs of an impending problem.

Some of the earliest automated monitoring systems were dedicated to expensive steam-driven power turbines. Data received directly from a machine is stored on a hard drive, buffered and presented in a variety of graphs that depict exactly what is occurring within that machine. Maintenance engineers and machine specialists suddenly had new information for analyzing changes in the machine’s operation.

When properly interpreted, these signals will pinpoint the location, nature and severity of developing problems. Data from automated monitoring systems enable plant personnel to predict with greater accuracy when a machine will need maintenance to prevent damage and avoid lost production. Machinery health management recognizes the significance of each machine in a production environment, focusing greater attention on those machines that, if stopped, would likely shut down all, or a major section, of the plant. On-line monitoring ensures that machine condition is being assessed continuously.

Performance monitoring

Another application is performance monitoring that compares machine performance with a thermodynamic efficiency model. Compressors, boilers and steam or gas turbines are the most commonly modelled types of equipment, but a thermodynamic model can be applied to literally any plant machinery. Performance deteriorates mainly due to fouling or a build-up on the blades and other surfaces, resulting in greater energy usage and lost throughput.

Equipment performance monitoring systems use existing process measurements, pass them through the thermodynamic model and provide a true picture of how well that machine is actually performing. While plant personnel may be aware that equipment performance is below normal, they may not know the significant cost of lost heat rate and excess energy usage. Such data can help lead to the root cause of degradation.

The most important element of performance monitoring is the expertise required to build the thermodynamic model and then distil and validate the large amount of input data. By utilizing the performance model to analyze this information and formulate actionable recommendations, performance specialists are able to identify lagging performance that has not been recognized by either production or maintenance personnel.

Because the model input data comes from the existing process measurements commonly found already in the plant, the data can be analyzed by either an on-site system or remotely using off-site specialists. Analysis based on thermodynamic modelling also enables a specialist to predict when a piece of equipment needs to be taken out of service for either the recovery of lost efficiency or a comprehensive overhaul. A machine’s future performance is evaluated, based on its history, in order to predict when the efficiency of that unit will drop below a certain financial or performance threshold, signalling when it should be taken out of service. In this way, performance monitoring complements prediction monitoring.

The CSI 6500 Machinery Health Monitor is Emerson’s on-line machinery monitoring solution, which combines API 670 protection with prediction and performance monitoring software through AMS Suite. Capable of being fully integrated with existing process automation systems, the CSI 6500 is specifically intended for automation and protection system upgrade projects.

Pulling it all together

Let’s look at how a complete solution like the one described in this paper would work in a typical turbo machinery application. In Figure 1, the sensors mounted to bearings provide a continuous flow of vibration measurements. A large turbo generator may have more than ten bearings with two sensors at each bearing plus other unique instrumentation – like speed, differential expansion and case expansion sensors.

Figure 1: The mounting of the sensors provides a continuous flow of vibration measurements

There could be as many as nine different types of measurements at various locations along a machine train. The cables leading from these sensors are connected to on-line monitoring hardware that is the foundation of a complete online solution.

By measuring for detailed vibration, in addition to peak vibration, the turbo machinery protection system, which is intended as a retrofit on shutdown systems, can monitor machine parameters for the signs of developing problems so that vibration never gets to the level where “protection” – i.e. shutdown – is necessary.

However, in the rare scenario of a rapid degradation, the machine is still protected. Machinery health parameters are integrated with the plant’s control system. Now vibration monitoring becomes an extension of the central control system, which often monitors variables such as temperature, pressure and load, any one of which could be symptomatic of a problem. Vibration monitoring actually monitors the position and the motion of the shaft inside the bearings.

That information is now integrated with the control room, making operators aware of what is happening deep inside a critical machine. Such information is of much greater value than just the symptoms of degrading performance.

Up to 50 per cent of machinery problems are process induced. If they are not caused by operators directly, they are the result of standard procedures used by control room personnel. When adjustments are made under these conditions without machinery health feedback, trade-offs occur. Improvements are made to production, but operations personnel are blind to the stress placed on machinery health.

When operators have real-time supervisory and vibration parameters at their disposal, they can observe the impact of process adjustments on a machine’s health and learn what steps can be taken to actually improve performance.

For example, during the start-up of a turbine, if case expansion or rotor eccentricity levels are not within acceptable limits, operators can make real-time adjustments to ramp rates and also make business decisions to optimize the ramp rate versus the impact on machinery health. Informed real-time decisions are best made when vibration data is integrated with the process automation system.

Application examples

Machinery health technologies together with protection systems are installed in various power plants around the world to improve reliability and give specialists better control over machinery condition. Installations include those at a northern European peaking power plant and at a central European coal fired power plant.

The peaking power plant was not only interested in protecting its equipment from a catastrophic machinery failure, but also in ensuring it could smoothly start up its 340 MW steam turbine and feed water pump to generate contracted high demand energy during the winter and summer peaks.

It quickly learned that the best way of fully utilizing its on-line prediction system was not only to define early machinery malfunction but also to compare start-ups historically. The first installation was quickly repeated on the second unit.

The central European power plant installed modern on-line prediction systems to manage predictable events and better plan shutdowns of its four large steam turbines and associated feed water pumps. Since the diagnostic specialists are only in the plant during daytime working hours, they became reliant on the prediction system’s recording capability, which allows for the real-time playback of events from an onboard hard disk. These transient events are treated with exact data collection precision, importance and priority as the start-up/shutdown events.

This is one of the key differences between modern predictive systems and those of a previous generation that were replaced in this plant. The other key reason why the plant installed a new prediction system was the opportunity of tying in and correlating multiple machinery health technologies under one software platform.

The plant’s on-line prediction system is complemented by off-line portable data collectors to manage the balance of plant machinery. A single machinery health management platform now provides specialists with all on-line and off-line machinery health data for the plant’s most critical elements (steam turbines), its essential elements (feed water pumps), and the balance of plant machinery (small redundant circulating pumps).

The power industry is not the only place where on-line machinery health management technologies offer users significant advantages. Some production or process plants derive most of their energy from waste heat and only purchase power from the local power grid when necessary.

One example is a coke processing plant where an on-line protection system on the power generation steam turbine would sometimes trip for no apparent reason, forcing the purchase of electricity from the grid at a high cost. Installing an on-line machinery health system immediately saved 120 hours of electric power grid purchases through continuous generation.

Information to act upon

For the most critical rotating equipment in the plant environment, three scenarios must be accounted for: the unpredictable, the predictable and the controllable. A complete solution must cover all three scenarios by providing protection monitoring, prediction monitoring and performance monitoring all integrated with the process control system. Monitoring systems utilizing advanced predictive technologies are giving end users newer, faster and more complete methods for analysis and automated analysis – information that can be acted upon.

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