Predictive Maintenance Benefits Maintenance Strategy at Linde Gas Plants

The Linde Group is a leading gases and engineering company operating in more than 100 countries. With such large and widespread operations, it is no surprise that maintenance activities are well run and that the company is no stranger to advanced monitoring techniques and equipment.

In the United States and the United Kingdom particularly, but also elsewhere in the world, online machine monitoring has been used for a number of years at Linde plants. And, at the company’s Shanghai headquarters, it has a large and impressive “remote operations center” where it monitors and tracks the process operations of all its major gas plants in China 24 hours a day.

But Henry Aung-Kyi, Linde reliability engineering manager for Greater China, also knows that the key to successful maintenance operations is the right balance of techniques, systems and methodologies. An experienced and knowledgeable engineer, Aung-Kyi is convinced of the value of highly sophisticated online monitoring for critical machinery.

But for some of the equipment at his gas plants, this level of technology and cost is not needed for optimum management of his company’s resources. And he has, for a long time, believed in the value of predictive maintenance (PdM), even if there is a price to pay in terms of time to acquire the skills and train the workforce.

Many process and manufacturing plants operate preventive maintenance (PM) activities. PM activities are usually time-based, with machine adjustments and replacements parts being made on a routine basis without regard to the operational performance or condition of the major piece of equipment or its component parts.

However, predictive maintenance is a condition-based approach that builds a picture of the anticipated condition of a component or piece of equipment based on trend data taken while the equipment is in service. Such data is mostly vibration analysis data, but can include lubricant analysis, temperature measurements, etc.

Based on this trend data, and applying principles of statistical process control, an engineer can predict when it is the best time to carry out maintenance activity. This allows him or her to plan work when it is most cost effective with a full understanding of the relative performance loss at risk.

Since most predictive maintenance actions are made while equipment is in service, there is minimal disruption to production and can result in substantial cost savings and higher equipment reliability.

Integrating into Linde’s High-Performance Organization

When Linde Group acquired the BOC gases company in 2006, it embarked on a program of integrating its new assets in China into Linde’s “High-Performance Organization”.

With Linde operations and maintenance being process driven, focusing on process optimization and continuous improvement, it was only natural for Aung-Kyi to assess the situation at each plant separately and develop a plan for improvement to the next level of technology.

The long-term goal is to bring all maintenance activities at all plants to the same advanced level, and share information and knowledge for optimal benefit. But, that will take time due to differences in the existing experience at each plant and the required level of learning needed by the workforce at each plant.

So, the Nanjing plant (a joint venture with Sinopec SPC) was the first plant selected for a pilot application of a predictive maintenance program. Before running the pilot application, Linde invited specialist companies, including local Chinese suppliers, to do some predictive maintenance service work at the Nanjing plant and propose a long-term program along with details of the services provided and the costs and anticipated benefits to Linde. After one year of trials and discussions with the competing vendors, Linde selected SKF to start up the PdM activity for its operations in China.

“A number of key points separated SKF from the other vendors,” Aung-Kyi said. “As well as providing excellent services in the actual monitoring of the plant, they showed their experience by identifying what equipment to monitor and went on to produce work recommendations that were far broader than the other vendors. Naturally, the local Chinese vendors had a price advantage, even though the SKF people are from the local SKF China organization.

But from SKF, we got more than just a ‘condition report’. We got constructive feedback of a depth that local suppliers could not match and a feeling that they really knew the machinery under investigation and had as much desire as Linde to manage that equipment efficiently.

That transmitted a huge amount of confidence to the local Linde engineers, which is really needed to get predictive maintenance understood and implemented properly. It wasn’t enough that I was satisfied with a particular vendor. What was really important was that the plant engineers in Nanjing wanted the vendor, and effectively it was joint decision between me and the actual Linde people receiving the service to select SKF.”

Consolidating and widening predictive maintenance in the China region

There are now nine Linde plants in China running predictive maintenance programs. The SKF engineers make a visit every three months to the plants to take measurements and follow up with in-depth discussions and recommendations as necessary.

All data is uploaded to a central Linde database where all plants can access, and benefit from, the historical trending data for the particular equipment being monitored, many of which are applied in more than one plant.

Simon Chen is Linde’s condition-based monitoring specialist for Greater China, and he has many years of experience in condition monitoring applications on many kinds of rotational machinery (e.g., compressors, motors, gearboxes, fans, pumps, etc.).

He oversees the internal coordination of knowledge transfer between the nine plants to ensure the company extracts the maximum benefits in the shortest time. During the process of delivering the service to these plants, Chen’s experience has assisted greatly to enable SKF reliability engineers to make effective condition-based maintenance monitoring to Linde. This is enhancing the overall quality of SKF service.

The main equipment being monitored is the high-speed compressors and ancillary equipment such as pumps and expansion machines. Readings are taken at positions on the compressor that indicate the condition of each of the compressor stages.

This is important to the overall efficiency of the compressor. Using SKF’s portable data collector Microlog CMVA60 or CMXA70, measurements are routinely and easily collected without any disturbance to operations.

Relevant data is also compared with that collected by the integrated distributed control system (DCS) in the compressor. The DCS data is needed to control and protect the production process. It measures flows, pressures, shaft vibrations, etc., so that very fast automatic decisions can be made depending on what is measured. Any strong deviations between the SKF data and the DCS data are then discussed to the point of deciding what corrective action is to be taken.

Success and a growing cooperation

Commenting on the situation now, Aung-Kyi said, “At the time that the pilot application was started, Linde had a lot of projects running, so the success of the pilot was very much in the ‘own hands’ of the vendor company.

They had to really work hard to inform, educate and train the local Linde engineers how to handle and assess their machine data in order to get the most understanding how to go further. The standards set by SKF were very high, also with respect to understanding the necessary safety standards, which was, and still is, a growing priority throughout Chinese process and manufacturing industries.

“Today the confidence in predictive maintenance routines and associated technology is beyond question in Linde. We have seen a gradual understanding that PdM is a very vital part of a maintenance strategy, especially because processes like our gas production are particularly suited to predictive maintenance.

Machines and equipment normally run well for a very long time. Problems come later when the first signs of deteriorating performance are caused by wear, unbalance, misalignment, overheating, etc. By detecting these conditions early enough, we can monitor and run the machinery at optimal acceptable performance without risking catastrophic breakdown that would disrupt production and have huge downtime and cost consequences.

“We also got to benefit from the SKF global experience of doing maintenance assessments on many process plants, including those that use many of the different makes of compressors, motors, pumps, gearboxes, etc., used in our plants.

Their global asset performance and maintenance database is accessible to their China colleagues and is a living repository of valuable information, being added to every day, that is almost impossible to acquire by client companies such as ours. We can look at SKF best practices for machinery operation and use that as a guide to what to expect from our existing and new machinery as we expand our business.”

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