Traditionally, predictive maintenance is used solely as a maintenance management tool. In most cases, this use is limited to preventing failures. Although this function is important, predictive maintenance can provide substantially more beneﬁts by expanding the scope or mission of the program.
As a maintenance management tool, predictive maintenance can and should be used as a maintenance optimization tool. The program’s focus should be on:
1) Eliminating unnecessary downtime, both scheduled and unscheduled
2) Eliminating unnecessary preventive and corrective maintenance tasks
3) Extending the useful life of critical systems
4) Reducing the total life-cycle cost of these systems
One of the most complicated and time-consuming components of predictive analytics is managing secondary Asset Operational and Maintenance data to get it in a format that is amenable to predictive analytics. Predikto deals with secondary data exclusively. By secondary, we mean that the data comes from our clients’ EAM / CMMS / Operational Indicators data storage and we have no control over how the data was collected and organized historically. ERP, EAM, CMMS, and other operational databases are typically not able to export raw data in a format required for the sorts of analytics that we do. Database designers don’t think about such complex analytics when designing a storage protocol; why would they? Their job is about data entry and collection, not about subsequent analyses that are oftentimes very complicated.
There is a considerable amount of custom computer programming that we have to develop in order to make our client’s data usable, which subsequently has to be done for each different asset (unless we’re dealing with identical machines that capture identical sensor data). In the past, we’ve experienced variations in how the same data is sent to us. Clients to try massage the data to make it easier to interpret (e.g., building in headers, or re-naming fields), but this complicates things for Predikto. It is critical that we get the exact same data every time in an absolutely raw and unadulterated way to ensure the complex data transformation and statistical models work every time as designed.
A limited amount of effort is made to determine the influence of system variables, like load, speed, product, or instability on the individual component failures analysis. These variations in process variables are often the root-cause of the observed mechanical problem. Unless analysts consider these variables, they will not be able to determine the true root-cause. Instead, they will make recommendations to correct the symptom (e.g., damaged bearing, misalignment), rather than the real problem.
Predikto takes into consideration historical maintenance (PM and Reactive) in addition to operating loads (speed, specs, material, etc.) when developing predictive models to understand when future failures / delays will take place.
Predictive maintenance techniques help determine the condition of in-service equipment in order to predict when maintenance should be performed. This maintenance technique offers cost savings typical time based preventive maintenance.
The main value of Predicted Maintenance is to prevent unexpected equipment failures while maximizing resources. The key is “the right information at the right time”. By knowing which equipment needs maintenance, maintenance work can be better planned (spare parts, people etc.) and what would have been “emergency maintenance” are transformed to shorter “planned maintenance”. Other advantages include increased equipment lifetime, increased plant safety, fewer accidents with negative impact on environment, and optimised spare parts handling.
Predictive maintenance utilizes nondestructive testing technologies such as infrared, acoustic (partial discharge and airborne ultrasonic), corona detection, vibration analysis, sound level measurements, oil analysis, and other specific tests. New methods like Predictive Analytics use actual meter and indicator readings from the machinery to predict failures. Predictive Analytics is the type of statistical methods used by Predikto to help maintenance teams perform preventive maintenance and avoid a failure.
The chart below came from a Predictive Maintenance paper. You can find the entire write up at: http://www1.eere.energy.gov/femp/pdfs/OM_6.pdf
McKinsey Global Institute released a study this year stating that the world will need $57 Trillion (with a T) infrastructure projects by 2030. That spending is just meant to repair our existing infrastructure, which is in a fairly abysmal state in the U.S.
The most recent “report card” from the American Society of Civil Engineers gives the U.S.’s infrastructure a “D” overall and estimates that we need to spend $2.2 trillion over the next five years to get it up to snuff.
The article also discusses an interesting approach by Chile to prioritize infrastructure projects based on predicting the highest need. A bulk of the infrastructure needs where in areas like roads, power grid, water, and telecommunication. The US resembles countries like South Africa and China instead of other developed countries like Germany and Japan.
Advanced manufacturing and predictive techniques can help reduce the annual spend from $2.7 Trillion to $1.7 Trillion, but we like the overall theme of the report in that we need to get smarter at tackling these big infrastructure problems. Similar to reactive vs. predictive asset maintenance, our infrastructure improvement plans should be more predictive and reactive.
Many industrial manufacturing companies run a reactive maintenance organization. That means they wait for things to break before replacing parts. Studies have proven that running a preventive maintenance plan reduces expenses by up to 75% in the long run. The challenge is that many of the Preventive Maintenance schedules were developed on intuition. Changing the oil filter once a year is enough. Most organizations are sitting on a wealth of data like prior downtimes (failures), work orders, parts, and the rich information captured in the indicators / meters.
Predikto has the skills and technology to analyze the maintenance data in order to provide a preventive maintenance plan and predictions to help reduce failures and reduce the overall cost of maintenance.
With Predictive Maintenance, organizations can spend less time and fewer resources repairing things too early or avoiding fixes when it is too late. Industry studies have documented cost reductions in the 3-5X range for catching problems early as opposed to catching a problem once there is significant damage to the equipment. Companies can spend more time focusing on what will happen next and be smarter about the preventive maintenance. It is much easier to fix a problem before it happens rather than reacting after the production floor has come to a screeching halt.