Driving Maintenance Workflows with Condition Monitoring

At the Emerson Exchange conference in Austin, Emerson’s Will Goetz presented, Best Practices for Using Condition Monitoring to Drive Maintenance Workflows. His abstract:

A well designed condition monitoring system can find the early warning signs of failures but what happens next is equally important. This session will explore real world failures to develop the best practice model for using condition information to drive maintenance work. It will use Doc Palmer’s time study analysis to illustrate the productivity potential of planning & scheduling and demonstrate the impact of these practices on the MRO supply chain. In addition, it will explore the use of criticality/risk models in prioritizing work and explore the business impact of the best practice model.

Emerson's Will GoetzWill opened defining top quartile reliability performance and three key function these top performers perform. He showed a bar chart showing the companies with the highest availabilities in the top quartile have much lower maintenance costs than companies in the other quartiles. In fact, 4th quartile performers spend 3.5x what top quartile producers spend in maintenance. These numbers are based on a 2013 Solomon RAM study.

Just by cutting costs to reduce maintenance costs to top quartile levels without fundamental changes would likely result in undermanned, fire-fighting maintenance modes of operations with a fall into the 4th quartile.

Top quartile performers analyze asset health, plan repairs and optimize reliability performance in an ever-improving cycle. Broken down further, a full reliability strategy includes asset master data, maintenance procedures, process parameters, condition indicators, asset health analysis, work identification, planning/scheduling/work execution, supply chain management in a continuous cycle to improve overall safety, availability and profitability.

Asset failures have safety, environmental, quality, production, and/or maintenance cost impact. Moving to a proactive maintenance model requires rethinking the workflow and maintenance culture to make sure the early warnings are acted upon in a timely manner.

Time-based maintenance programs are typically ineffective because failures are largely random. Will showed a number of failure curves including bathtub curves, slow aging, best new, constant random and worst new. Time-based maintenance does not improve performance for the majority of these curves.

Once a failure is detected failure mode effects analysis (FMEAs) help identify the cause and plan the right corrective action. Best practice is failure analysis, detection method selection, failure detection, work identification, planning, and failure coding. For top quartile performers, nearly all maintenance resources should be executing planned work, instead of reactive work. Condition monitoring must drive 50% of this planned work.

Top quartile performers spend most of their maintenance time and effort performing planned activities. The results of these efforts is greater availability, less operational risk from time spent in unplanned shutdown and startup modes of operation, and lower maintenance dollar spend.

You can connect and interact with other reliability and maintenance experts in the Reliability & Maintenance group in the Emerson Exchange 365 community.

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Wireless for High Temperature Electric Arc Furnace Control

A key part of the Industrial Internet of Things is the sensors that provide the measurements that feed decisions and actions. At the Emerson Exchange conference in Austin, Emerson’s Cheng Vue presented, When the Heat is On, Control with Wireless. His abstract:

Many measurement challenges exist when monitoring high temperature assets such as arc furnaces. These challenges include high temperature, moving platforms, and high electromagnetic interference (EMI). Many steel mills use these points for control where their only solution was shutting the furnace down and replacing wires often. By implementing an Emerson Wireless technology, the case study customer was able to reduce or eliminate all of these measurement challenges, resulting in greatly reduced maintenance costs while providing additional throughput.

Emerson's Cheng VueCheng opened describing a scrap steel mill process which had extreme temperatures up to 3000 degF with ambient temperatures of 140 degF and high electromagnetic interference. He showed a video of an electric arc furnace process that heats steel scraps by an electrical arc. The molten steel is then removed with a ladle process. The process is performed in batches.

On average temperature sensors had 60 feet of wiring to the junction box and then 300 feet from the junction box to the control system. These cables were exposed to the 140 degF ambient temperature.

The temperature sensors are monitoring for hot spots and potential runaway temperatures. The temperatures monitor the burners to help prevent burner failures. The loop controls the electrical energy going to the electrical arc probes to control the temperatures.

Challenges included inaccurate measurements from EMF interference on temperature sensor wiring, wire degradation from heat, rewiring up failure of wiring and downtime of the process during these maintenance activities.

The solution for these challenges was to use wireless temperature transmitters. The transmitters for a mesh network to provide data back to the control system. Rosemount 648 wireless transmitters provided 1 and 2 second updates required for the control strategies. Rosemount 848T transmitter devices were used to collect the sensor data which can take in up to 4 inputs and transmit the values wirelessly back to the gateway. The sensors connected to the 848 provided 4 second update rates.

Dog houses were used to protect the 848T transmitters to protect molten slag from splashing on the device. The back of the dog house is open to provide the wireless antenna a path to connect with other devices to form the mesh network.

Using wireless devices, wiring was minimized just from the sensors to the transmitters. These were ultimately eliminated when they changed over from the 848T to the 648 transmitters. This also helped to mitigate the electromagnetic interference with the elimination of wires. For this application robustness is everything given the harshness of the environment.

In financial terms, additional throughput in the tens of millions was possible. Reduced unscheduled downtime also amounted to a 20% gain. Cost savings from reduced maintenance costs measured in the hundreds of thousands of dollars.

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Renewable Energy Powering Gas Wells and Pipelines

At the Emerson Exchange conference in Austin, an oil and gas producer shared a story of automating natural gas wells and pipelines with renewable power sources.

The presenter opened describing some of the choices for renewable energy including solar panels and wind turbines. Based the latitude of the production fields, there was barely enough sunlight for the solar panels to collect and created electrical energy. They were also challenged with average winds speeds of only 4 meters/second. This was not enough wind on a consistent basis.

Of the two, the solar panels were a better option since they collected 90%+ of the energy in tests they conducted.

ControlWave Express RTUThe architecture of the SCADA system is a ControlWave Express remote terminal unit (RTU) connected to the solar panel and battery bank collecting data from pressure and temperature transmitters. 75 watt solar panels were used to charge the battery banks. On some other installations with larger RTUs required, 400 watt solar panels were used.

For the natural gas pipelines, more I/O parameters were collected. These I/O were not only for monitoring, but for control in actuating valves along the pipeline. Data was transmitted on a real-time basis through radios to central facilities. The 20-watt radio was turned on a periodic basis to batch transmit data collected in the RTU.

One issue was snow covering the panels. Panels were mounted vertically to avoid snow accumulation. Even at this mounting angle, they could capture enough solar energy to recharge the battery banks. Energy use had to be carefully monitored and minimized so the batteries would not discharge fully in times of lack of sunlight.

You can connect and interact with other SCADA and RTU experts in the SCADA group in the Emerson Exchange 365 community.

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Emerson Exchange Conference Video Highlights from Day 1 and Day

Here are short video recaps from day 1 and day two of the Emerson Exchange conference.

More fun and action in store today!

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Improve Safety Proof Test Capabilities with Intelligent Instrumentation

At the Emerson Exchange conference in Austin, Emerson’s Afton Coleman, Erik Mathiason and Tonya Wyatt presented, Improve Safety Proof Test Capabilities with Intelligent Instrumentation. Their abstract:

Safety Instrumented Systems are designed to be the most critical line of defense against a catastrophic failure. Ensuring that each component of the system is working properly is required, often leading to downtime and significant maintenance costs. Utilizing advanced features in Emerson’s intelligent instruments, such as smart meter verification and partial stroke testing, users can monitor for common failures in their safety instrumented function while also extending the period between proof tests.

safety-proof-test-intervalAfton kicked off this introductory level workshop explaining the concept of Probability of Failure on Demand (PFD), proof test intervals and the benefits of diagnostic technology for safety instrumented systems. A safety instrumented system consists of a sensor, logic solver and final control element. Unlike a basic process control system, it’s job is to take the process to a safe state based upon a demand.

The most dangerous condition is an undetected failure mode where a safety demand does not result in an action to take the state to a safe state. When designing a safety instrumented function (SIF) or safety loop, a safety integrity level or level of risk reduction defines the requirements for the loop. When designing a SIF, the failure modes for the sensor, logic solver and final element must be assessed for their failure upon demand. Failure rates of individual components can be combined to calculate the overall PFDavg. Continue Reading