Predictive maintenance is a key component of Industry 4.0. Poor maintenance strategies can substantially affect the operational efficiency and profitability of industrial manufacturers. To be competitive, companies in asset-intensive industries need to minimize unplanned downtime and optimize maintenance costs.
For the manufacturing industry, using data to enable and improve predictive maintenance is particularly relevant as the use case can be applied to any kind of manufacturing production system, such as computerized numerical control (CNC) infrastructure, supply chain and warehouse systems, logistics and test systems, and so on.
While a wide variety of data sources may be used depending on the specific application, the keys to shifting from reactive to predictive maintenance are Internet of Things (IoT) data streams or machine-to-machine (M2M) messages sent and received through an MQTT (the IoT messaging standard) broker or provided by historians from operational intelligence systems. These are the sources of the raw data required to assess whether maintenance operations are needed; however, data from other sources is necessary to establish a proper predictive maintenance system. For instance, maintenance management systems contain information about the pieces of equipment themselves, such as maintenance reports. Other sources of data include supervisory control and data acquisition (SCADA) systems, a special repository containing media files (such as pictures and video streams), maintenance manuals, and weather forecasts. The variety of data that can be used in predictive maintenance is vast.
The architecture presented here demonstrates how recommended Oracle components can be combined to build a full analytics architecture that covers the entire data analytics lifecycle, from discovery through to action and measurement, and delivers the wide range of business benefits described above.
Our solution is composed of three pillars, each supporting specific data platform capabilities. The first pillar provides the capability to connect, ingest, and transform data.
There are four main ways to inject data into an architecture to enable manufacturing organizations to move from reactive to predictive maintenance.
Data persistence and processing is built on three (optionally four) components. Some customers will use all of them, others a subset. Depending on the volumes and data types, data could be loaded into object storage or loaded directly into a structured relational database for persistent storage. When we anticipate applying data science capabilities, then data retrieved from data sources in its raw form (as an unprocessed native file or extract) is more typically captured and loaded from transactional systems into cloud storage.
The ability to analyze, predict, and act is facilitated by three technology approaches.
The multiple models created by combining data science with the patterns identified by machine learning can be applied to response and decisioning systems delivered by AI services.
The final yet critical component is data governance. This will be delivered by OCI Data Catalog, a free service providing data governance and metadata management (for both technical and business metadata) for all the data sources in the data platform ecosystem. OCI Data Catalog is also a critical component for queries from Oracle Autonomous Data Warehouse to OCI Object Storage as it provides a way to quickly locate data regardless of its storage method. This allows end users, developers, and data scientists to use a common access language (SQL) across all the persisted data stores in the architecture.
With predictive maintenance, equipment is serviced only when it needs to be serviced, reducing unexpected outages. This delivers multiple advantages that include fewer scheduled maintenance repairs or replacements, the use of fewer maintenance resources (including spare parts and supplies), and, simultaneously, fewer failures. These proactive predictions can help to prolong the life of equipment while reducing the risk of potential product delays by minimizing equipment changeovers and the associated downtime.
Reducing unplanned downtime helps optimize business operations, improving efficiency, productivity, and speed and helping ensure the right part gets to the right place at the right time. Meanwhile, reducing maintenance, labor, and material costs and optimizing asset lifecycle costs increases profitability.
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