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If you're reading this page, you'll recognize yourself when we say that any organization that works with real physical assets is faced with a significant challenge: keeping those assets in good working order. One failed asset could stall productivity, impact revenue, or even pose a threat to people’s medical well-being. But how are those assets maintained? What process is used?
More often than not, asset maintenance is performed using a traditional time-based approach. For example, a diesel generator may be serviced every 4 months — the actual status of the generator, however, is not taken into consideration. Another common policy is to use a count-based approach; for example, all pipe fittings may be replaced every 8 months — whether it’s needed or not.
The problem with these two approaches to maintenance management is that they are not cost-effective and are labor-intensive. Decisions are based solely on time instead of actual machine data... this results in wasted parts, machine fatigue, lost man-hours, a degradation of equipment performance, and unexpected freezes in production.
There is another option, however, that can save both time & money: predictive maintenance. Predictive maintenance solutions use machine data to determine the most efficient time to perform maintenance. The “most efficient time” is before the asset loses performance capability (threshold based) while remaining cost-effective. How does this work?
Machine assets are monitored and raw data is collected. The monitoring process can be facilitated via a number of different technologies, such as vibration analysis, wireless sensor networks, and so on. Data Science Studio (DSS) cleanses/formats this raw data and uses modeling algorithms to determine the most efficient time to perform maintenance. The core concept here is predictive maintenance analytics: DSS informs management of expected maintenance issues before they happen, effectively negating the need to constantly fix broken equipment. This approach makes a significant impact to an organization’s bottom-line.
Data Science Studio (DSS) is a powerful advanced analytics platform that enables your organization to build data products that make sense of raw data. Instead of blindly performing non-predictive maintenance techniques and wasting money, why not let your machine assets tell you when they need maintenance? By combining asset monitoring technology with DSS, you can not only retrieve critical data, but you can design & create predictive maintenance models to determine potential issues before they become expensive problems.
After you've connected to your data source(s) with DSS, build automated data processing pipelines where relevant data is extracted from extraneous information. This cleansing process formats the data and combines it to prepare a dataset that is ready for further analysis. When the data is in a ready-state, you can now create, build, and run advanced predictive maintenance models on your data in order to be alerted of future problems, such as performance deficiencies, failing equipment, and mechanical breakdowns. Knowing what’s going to happen to your valuable assets before it occurs is invaluable to the smooth flow of your organization. Predictive maintenance analytics from DSS empowers you to take control of your asset management and take proactive action when needed.