Mercedes-Benz U.S. International (MBUSI), an SUV and sedan plant in Vance, AL, was undergoing some organizational changes in August 2011. Ken Hayes had rotated through several senior management positions throughout Mercedes and was returning to maintenance and engineering after eight years managing body and assembly production operations. He was dissatisfied by a lack of growth in the maintenance systems and decided to benchmark other Daimler facilities to see if there were practices he could apply at MBUSI. Realizing maintenance challenges were very similar in the other plants, he searched for a different approach.
One of the goals of reliability is to identify and manage the risks around assets that could fail and cause unnecessary and expensive downtime. Organizations know it is important to identify areas of potential failures and rate them in terms of likelihood and consequence. They also have put in place good reliability strategies and have implemented proactive, condition-based maintenance programs. But now, machine learning is helping maintenance organizations get to an elevated level of situational intelligence to guide actions and provide early warnings of impending asset failure that previously remained undetected. Machine learning is paving the way for smarter and faster ways to make data-driven decisions in predictive maintenance (PdM).
What’s worse, a disruption in a major city’s rail system or extended downtime in a cloud data center?
The answer, of course, depends on your perspective. If you’re one of thousands of people who use the rail system to get to work and you can’t afford to miss a day, that disruption is no small matter. But, if your business relies on the Cloud and you’re losing thousands of dollars for every minute of downtime, you might consider your situation more serious than that of the stranded commuters.
DC Water’s Blue Plains Advanced Wastewater Treatment Plant is the largest advanced wastewater treatment plant in the world. It covers 153 acres and has a capacity of 384 million gallons per day (MGD) and a peak capacity of 1.076 billion gallons per day. This massive facility, commissioned in 1937, consists of hundreds of rotating assets that must operate efficiently to effectively support the needs of customers in a multi-jurisdictional area.
The goal for maintenance managers is simple: Oversee the successful installation, repair and upkeep of the facility’s assets for smooth operations and on track budgets. This goal is certainly obtainable in an ideal setting where inventory is always in stock, technicians are continuously efficient and assets are always running.
Achain is only as strong as its weakest link. Many businesses address this issue by focusing efforts on identifying and strengthening the weakest link. But, is this the best solution? Rather than accepting the existing chain with its weaknesses as given, reconfiguring or redesigning the whole chain can potentially eliminate the weakest link altogether.
This is the reasoning behind product value management (PVM), a holistic approach that can help asset managers redesign assets to boost performance without adding lifecycle costs or complexity.
Detecting wear, imbalance and misalignment of rotating parts within machinery is critical to its health and overall performance. This can be achieved by implementing a variety of proven techniques. Vibration analysis, for example, uses accelerometers to detect potential problems with industrial equipment caused by incorrectly aligned, loose, or unbalanced rotating parts.
Maintenance and reliability teams, programs and practices seem to be a constant target for cost reductions, but this brings up an important question: How can it be accomplished without losses to equipment effectiveness and asset integrity?
When companies implement overarching programs to reduce equipment failure, it isn’t always apparent where the problems and root causes exist because the programs often attempt to address everything at once. Some immediate returns and benefits are realized, however, these programs often do much more than is actually required. An example includes following the original equipment manufacturer’s guidelines without taking into account or adjusting for site specifications and equipment layouts.
During the early morning hours of Wednesday, July 12, 2017, the skies opened up in Racine, Wisconsin, and over seven inches of rain poured onto the ground for several hours. At the Racine wastewater facility, which is accountable for purification and disposal of sewage and wastewater from over 200,000 people before pouring them into Lake Michigan, all hands were on deck.
As a result of the flash storm, the facility was faced with 106 million gallons of water in a 12-hour period, three times more than its designed capacity flow of 36 million gallons per day. When dealing with such considerable storms, which happen a handful of times a year, all the machines in the facility have to work at 100 percent capacity—there is no room for error. When you hear the words “unsung heroes,” reliability engineers should be included on that list. It is through their efforts that systems like the one in Racine keep floodwaters and impact to life and limb minimized.
During a recent visit to the Racine WasteWater Utility plant, Keith Haas, the facility’s general manager, pointed out that the plant has had zero days of unplanned downtime since 1970. That’s zero incidents in over 45 years. Even more surprising is that most of the facility’s equipment, which largely consists of pumps, has been there since the 1970s, as well.
How do plant workers reconcile the aging equipment with 100 percent reliability and uptime? Through the use of advanced technologies that enable them to predict malfunctions before they occur.
Microsoft Excel® is an amazing tool. Yet, it has its limitations and flaws for engineers who aren’t trained in computer programming.
The main problem with spreadsheets for managing maintenance programs is human error. No matter how fastidious you are when creating a spreadsheet, a single line of data that is entered incorrectly, or worse, an inaccurate user-defined formula, can have huge implications down the road.