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Predictive Maintenance for Gas Pipeline Compressors
CPG had a compression asset failure that interrupted service and had the potential to create customer dissatisfaction.
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Schneider Electric Le Vaudreuil factory in France is recognized by the World Economic Forum as one of the world’s top nine most advanced “lighthouse” sites, applying Fourth Industrial Revolution technologies at large scale. It was experiencing machine-health and unplanned downtime issues on a critical machine within their manufacturing process. They were looking for a solution that could easily leverage existing machine data feeds, be used by machine operators without requiring complex setup or extensive training, and with a fast return on investment.
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A large construction aggregate plant operates 10 high horsepower Secondary Crusher Drive Motors and associated conveyor belts, producing 600 tons of product per hour. All heavy equipment requires maintenance, but the aggregate producer’s costs were greatly magnified any time that the necessary maintenance was unplanned and unscheduled. The product must be supplied to the customers on a tight time schedule to fulfill contracts, avoid penalties, and prevent the loss of future business. Furthermore, a sudden failure in one of the drive motors would cause rock to pile up in unwanted locations, extending the downtime and increasing the costs.Clearly, preventative maintenance was preferable to unexpected failures. So, twice each year, the company brought in an outside vendor to attach sensors to the motors, do vibration studies, measure bearing temperatures and attempt to assess the health of the motors. But that wasn’t enough. Unexpected breakdowns continued to occur. The aggregate producer decided to upgrade to a Condition Based Monitoring (CBM) sensor system that could continually monitor the motors in real time, apply data analytics to detect changes in motor behavior before they developed into major problems, and alert maintenance staff via email or text, anywhere they happened to be.A wired sensor network would have been cost prohibitive. An aggregate plant has numerous heavy vehicles moving around, so any cabling would have to be protected. But the plant covers 400 acres, and the cable would have to be trenched to numerous locations. Cable wasn’t going to work. The aggregate producer needed a wireless solution.
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One of the world’s largest exploration and production companies was operating as leanly as possible given the prolonged slump in oil prices. As such, an operational parameter wasn’t effectively monitored and equipment failure went unnoticed in the machines.
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Heat Exchanger Monitoring and End of Cycle Prediction
Predicting end-of-cycle (EOC) for a heat exchanger due to fouling is a constant challenge faced by refineries. Proactively predicting when a heat exchanger needs to be cleaned enables risk-based maintenance planning and optimization of processing rates, operating costs, and maintenance costs. Before using Seeq, the engineer had to manually combine data entries in a spreadsheet and spend hours/days formatting and filtering the content or removing non-relevant data when necessary (for example when equipment was out-of-service).
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If a truck at an active site has a pump failure, Baker Hughes must immediately replace the truck to ensure continuous operation. Sending spare trucks to each site costs the company tens of millions of dollars in revenue that those trucks could generate if they were in active use at another site. The inability to accurately predict when valves and pumps will require maintenance underpins other costs. Too-frequent maintenance wastes effort and results in parts being replaced when they are still usable, while too-infrequent maintenance risks damaging pumps beyond repair.
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Planned Maintenance for Power Generating Company
Having no unplanned outages in multi-unit power plants throughout the late spring, the summer and the early fall months is challenging in the hot Texas weather. An unplanned outage during these months could mean having to purchase replacement power at spot market prices, which could be spiking during the outage. Knowing when and for how long to overload the equipment in power plants is a significant part of operating strategy. Operators need accurate estimates of motor driven pump and fan loading levels to determine operating limits.
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Wind turbines using digital technology
Issues involved in expanding commercialization of wind powerIn 2015, the worldwide capacity of renewable energy facilities exceeded that of coal-fired power.*1 With the aim of creating a low-carbon society, in July 2012, Japan put into effect the feed-in tariff scheme for renewable energy, stimulating the construction of solar and wind farms. In 2016, the full liberalization of the electrical retail business resulted in an increasing number of companies planning either to enter the power generation field or to expand their business. These market conditions engendered a need for the development, design, manufacturing, and sales of wind turbines optimized for Japan's environmental conditions. Utilities considering entering the field of wind power also sought assistance in the streamlining of maintenance and other such business operations.
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To increase efficiency, develop new services, and spread a digital culture across the organization, Enel is executing an enterprise-wide digitalization strategy. Central to achieving the Fortune 100 company’s goals is the large-scale deployment of the C3 AI Suite and applications. Enel operates the world’s largest enterprise IoT system with 20 million smart meters across Italy and Spain.
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Waterford Township has been faced with losing a significant number of DPW staff, some with more than three decades of water and wastewater knowledge, to retirement. The company began to search for a solution that would like real-time operational data from its SCADA systems to its CMMS and DMS to create standard operating procedures and work orders automatically when conditions were met in defined workflow procedures. One key aspect of the project was to get operating procedures standardized and in a format where staff in the field, who might not be familiar with the system, could follow the necessary steps to correct the issue.
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Detecting Cavitation And High Vane Pass Frequency For Pumps
The Condensate Cooling Water (CCW) pump, one of the critical pumps in maintaining steadystate operations, is a horizontal vane pump operating at up to 1650 m3/hr with a discharge pressure of 9 MPa (62 psi) at 986 rpm. Each day this pump is offline costs the plant $250,000 in lost revenue and each failure costs tens of thousands of dollars to execute an unplanned repair. Thus, Larsen & Toubro (L&T) really needed a predictive maintenance solution to detect faults at an early stage and provide a reliable prediction of Remaining Useful Life (RUL)
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In tandem with its 6 year-long smart meter rollout plan, Con Edison sought to implement Advanced Metering Infrastructure (AMI) operations on top of a comprehensive enterprise data analytics platform for improved operational insight and customer service for its base of more than four million customers. In order to improve customer service and operations across its region, one of the largest integrated utilities in the United States has rolled out the C3 AI Suite and C3 AMI Operations application on AWS. Con Edison’s project objectives were to deliver on the utility’s commitments for presenting customer data, establish AMI operations across 5 million smart meters to ensure operational health, and build a federated data image platform for analytic capabilities. The utility’s smart meter deployment will generate between 100 terabytes and 1 petabyte of data per year, so choosing a platform that could scale and continue to perform analytics on an ever-larger data set was vital.
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To extract shale oil and gas, specialized equipment is used to fracture rock via a process called hydraulic fracturing (or “fracking”). To do this efficiently, users must know when their equipment needs maintenance. If the equipment stops working while in the well, millions of dollars are lost due to downtime and logistics. Additionally, our client, a major oilfield equipment company, needed a way to make their product stand out. They wanted to accomplish this by providing oil and gas software solutions but had no idea on how to develop and deliver software in the cloud.
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Utility companies that operate hydro turbines have a vested interest in performing regular maintenance to prevent unexpected failures. Most maintenance occurs on a scheduled basis where the asset is taken offline, inspected, and repaired proactively if needed. Hydro turbine units are highly reliable, meaning that few examples of unplanned downtime exist. However, these failures are very costly to their operators.Given the sensitivity operators have to unplanned downtime, many have equipped turbines and generators with sensors and platforms to collect valuable performance information in real-time. But because there are so few historical hydro failures to compare against, rich streaming data and legacy statistics-based analysis are not very accurate at predicting true failure events. In fact, they often create more problems by overloading monitoring teams with benign false positives that result in unnecessary downtime to evaluate. This begs the question: Can artificial intelligence help maintenance teams extract more value out of their data?
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The challenge was to harvest and sift through this data, recognize the patterns that indicate a high likelihood of asset failure, identify the most urgent issues, and get the right information to its engineers with enough lead time for them to take effective action.“Before, we only used between 10 and 12 percent of the operational data we collected, which is the industry average,” comments Benn. “By the time we had searched for, collated and forwarded the right information to the right people, we might respond too late to avoid impact to operations, or have to make last-minute changes to our maintenance schedule, which reduces efficiency. Our challenge was to provide right-time, actionable, effective information proactively, rather than in a reactive or look-back assessment.”“What we wanted was a way to identify patterns in that sensor data that would give us an early warning of asset failure. We saw an opportunity to use analytics technology to extract greater value from the systems and data we already possessed, which would help us to, for example, avoid preventable failures and potentially save millions of dollars.
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Sean Simon is the SVP of Operations at CIG Logistics, where sand is transloaded and stored for third parties in the oil and gas industry. Before looking into CMMS solutions, his team spent three years trying to manage their maintenance operations with a paper-based system, leaving them with the major issue of not being able to gather or access data. “There’s no way to mine paper. There was no daily summary, no way of tying together comments or keywords.” As a result, trying to track and schedule preventive maintenance was nearly impossible. “It was like owning a car in the 1950s. You had to try to remember the last time you did something and guess at the maintenance that needed to be done in the future”.
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In late 2015, a deployed combustion turbine experienced a row two vane failure, which caused massive secondary damage to the compressor, resulting in nearly two months of downtime and up to $30M in repairs costs and lost opportunity. This failure, though rare, is representative of typical catastrophic events that are very difficult to catch. Though the onsite plant operations team had been monitoring the asset, this specific failure mode was previously unknown and very nuanced, and existing alarms did not have enough information for SMEs to properly diagnose it in time.The OEM decided to evaluate SparkCognition’s predictive analytics solution, SparkPredict®, with the following objectives:1. Demonstrate the ability to detect and distinguish operational and anomalous online steady-state conditions based on blind data provided from the turbine.2. Provide additional insights about the key contributing factors to the underlying anomalies.3. Provide a UI that interfaces to live streaming data from the asset.
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Continuous condition monitoring pays off at a large power utility
A large power utility in Hawaii was looking for more frequent condition monitoring on their Balance of Plant (BOP) generation assets. They had experienced significant equipment failures that occurred between their scheduled quarterly walkaround condition monitoring routes.
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Large-scale Implementation of Wireless Predictive Maintenance
In 2016, Arizona Public Service (APS) decided to enter the California ISO (CAISO) market, which allows them to sell power into the California market. One of their key assets was Sundance, a 420 MW unmanned peaker plant located 50 miles outside Phoenix. The entry into the CA energy market meant that starts tripled and run hours doubled almost immediately at the plant. They started looking for wireless Predictive Maintenance (PdM) system because the running hours were typically when no one was on site, which meant that traditional forms of PdM were not possible. Typically, a specialist would collect vibration and other condition data on equipment, but it had to be taken during operation, and it was difficult to get personnel out to the site.“Reliability was foremost on our minds,” commented Don Lamontagne, Supervisor of Equipment Reliability Engineering. “We faced huge loss of potential revenue, as well as fines if we weren’t able to generate power when it’s needed.”
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Gas Pipeline Improves Station Efficiency and Drives Revenue with DataRPM
In the capital-intensive oil and gas industry, businesses rely heavily on expensive assets that are deployed in harsh environmental conditions. From a drilling point in the sea to an intermediate station in the desert, the dynamic environmental conditions at each point along the long line affect the performance of the assets deployed along the line. The systems that are used to support these mission-critical assets must also be highly reliable, responsive and secure.One company that operated a long-distance gas pipeline encountered numerous challenges with the overall efficiency of its pipeline, ranging from sub-optimal usage to wastage of natural resources. Even with the optimal equipment and setup, the wide array of variables in operating conditions combined with the sheer distance covered by the pipeline made running the business difficult.In this case, there were 22 injection stations along the length of the pipeline, operating under very disparate conditions with different efficiencies. This made it difficult to identify the interdependent effectiveness of these injection stations, despite having a large data set on various parameters at each injection substation. Even a single instance of failure could cost the company hundreds of thousands of dollars in lost revenue as well as any additional costs for repairs that had to be made.The company was spending $5 million per mile of pipeline annually in corrective maintenance. Along with this, the loss of revenue due to the undelivered material was estimated at $250 million. With energy prices dropping, the loss in revenue directly reduced the bottom line of the company. With the clock ticking and revenue dipping, building a perfect efficiency improvement model became a top priority.
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