A predictive maintenance model uses sensors and IoT technology to monitor the condition of equipment and predict when it will need repairs or replacements. This equipment may be anything from personal cars to large factory machines.
The machine learning programs used to run these models tend to take either the regression approach or the classification approach. Regression produces more accurate results but relies on more data; classification best predicts equipment failure within a set time period.
Predicting maintenance needs of equipment results in more efficient—and, therefore, safer and cheaper—use of equipment. All users and owners of the equipment benefit from these models.
These models can also identify the exact cause of the equipment failure or degradation in real time, so fixing the problem takes no time at all.
Predictive maintenance models require sensors that record, in real time, the condition of all parts of the machine.
The model should also have historical data on the condition of the equipment, in as much detail as possible.
What may be considered essential or merely useful external data depends on the company or user. Personal vehicle manufacturers, for example, may consider weather data essential whereas managers of drywall manufacturing plants will not.
On the other hand, the amount of useful external data is large. Supply chain data, in particular, allows managers and users to arrange for repairs and replacements even faster.
Another useful source of external data may come from individual equipment users, particularly if a sensor fails to record something for whatever reason.
Other external data, like the location of the closest repair shop or new security threats in the area again depend on the industry.
One of the greatest challenges of predictive maintenance models is that they require a great deal of data to work most effectively yet the users of the equipment are usually not data scientists who are trained to cleanse the data. Carefully setting up the predictive maintenance model from the outset with trained data scientists, however, should reduce a lot of the problems of inaccurate or redundant data.
Perfectial: Predictive Maintenance: How Machine Learning Models Help Reduce Prevent/Repair Costs and Break Limitations of Traditional Maintenance Approaches
Oracle: 5 Use Cases for Predictive Maintenance and Big Data
Over the last years, shipping companies are increasingly facing the impact of a fast-changing competitive environment due to digital transformation. External factors such as regulations, cyber security threats and fluctuating TCEs have been dictating the pace of digital adoption and push shipping companies to adopt technology to date. Digitalization in shipping is still treated on an ad-hoc use-case basis, lacking a holistic strategy for digital transformation. However, advances in technology and availability of data is now allowing companies to transition down the digitization path more quickly.
Total Consumer Insights – U.S. Only dataset provides information regarding: Consumer Lifestyle Data,Marketing Attribution Data,Individual Data and more.
IBM PAIRS Services provides queryable geospatial and temporal data in the form of maps, satellite images, weather data, drone data, and other data.
Lepton Software’s dataset – ‘Road Networks – India – India Road Network, State Highway, National Highways, Route Numbers on Roads, Rail Coverage Network’ provides Location Data, and Traffic and Routing Data that can be used in and Predictive Maintenance
The Data Axle US Consumer Database dataset provides information regarding: Individual Data,Consumer Lifestyle Data,Identity Data and more.
Anova DataOnline Products & Services offer data management and hardware for oil & gas, liquified natural gas, rail, and other industries