Predictive Maintenance

Reduce downtime. Predict failures accurately. Service equipment only when necessary. Increase customer satisfaction.

WEBINAR

IoT and Data Prerequisites for Building the Smart Factories of Tomorrow

WHAT IS PREDICTIVE MAINTENANCE?

Predictive maintenance is a technique used in various industries to reduce machine downtime by predicting its failure. The technique is implemented using a combination of the following:

  • Real-time data ingestion from IoT devices
  • Extract-transform-load of this data and writing it into a data store
  • Developing machine learning (ML) algorithms to extract insights into failure events and training these algorithms using the stored data
  • Deploying the final algorithm(s) in production onto the target environment
  • Monitoring the performance of the system and tuning the implementation as physical conditions change over time

Data Pipelines for Factory IoT and Machine Learning

Watch this video to learn how MapR allows ingesting data sources from IoT devices, creating data streams, persisting them into our database, and eventually enabling interactive query analysis and machine learning.

WHY PREDICTIVE MAINTENANCE?

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CHALLENGES WITH CURRENT METHODS

  • Reactive Maintenance
    Most enterprises start with this method. Repairs and replacements are made to the equipment after a failure occurs. This is not an optimal solution as it costs at least 10x more to repair equipment after it fails.
    Moreover, any machine downtime directly impacts revenue and customer satisfaction.
  • Preventative Maintenance
    Enterprises consider this the next best solution after reactive maintenance. Equipment is repaired or replaced at predefined time intervals based on expert knowledge.
  • Enterprises often schedule repairs or replacements when nothing is actually wrong with the equipment, making this approach very expensive.
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ADVANTAGES OF PREDICTIVE MAINTENANCE

  • Predictive maintenance is the preferred method for manufacturers today.
  • With this method, manufacturers are able to schedule repairs or replacements exactly when it is needed.
  • If implemented correctly, manufacturers tend to achieve near-100% operational uptime for their equipment.
  • It is the perfect trade-off between productivity, cost, and uptime.

KEY ESSENTIALS OF PREDICTIVE MAINTENANCE

In order to get predictive maintenance right, a few key capabilities are required as part of the solution:

  • Adequate instrumentation of the equipment using IoT sensors
  • Real-time connectivity that allows a continuous stream of data coming from the on-board sensors
  • Ability to ingest unstructured machine logs from the enterprise business applications that manage the output of the equipment
  • A scalable, highly available database capable of storing structured, unstructured, and semi-structured data
  • Flexibility to use ML toolkits of choice for data scientists to develop ML models
  • Ability to train and deploy these models in-place, hence requiring no data movement
  • Ability to monitor the system performance for any drift that occurs over time and the capability to correct it without extensive data movement for yet another training cycle

BENEFITS OF USING MAPR FOR PREDICTIVE MAINTENANCE

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REAL-TIME STREAMING

MapR Event Store for Apache Kafka acts as the streaming system of record for producers and consumers to ingest real-time data from edge devices.

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EXTREME SCALABILITY

MapR Data Platform manages both structured and unstructured data. It is designed to store data at exabyte scale, supports trillions of files, and supports a wide range of workloads.

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FLEXIBILITY IN BUILDING ML MODELS

MapR integrates with Apache Spark. With support for POSIX, cutting-edge AI and ML tools like the new Python ML libraries can run natively on the same cluster.

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HIGH AVAILABILITY

Automatic failover ensures data is always available, so containerized applications run on a 24x7 basis.

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DATA PROTECTION icon

DATA PROTECTION

Protect critical data with mirroring, replication, and consistent point-in-time snapshots.

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HIGH PERFORMANCE

Meet your performance SLAs for containerized enterprise applications by flexibly deploying on NVMe, SSDs, HDDs, or cloud.

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PREDICTIVE MAINTENANCE WITH THE RIGHT PARTNER

RapidMiner brings artificial intelligence to the enterprise through an open and extensible data science platform. Built for analytics teams, RapidMiner unifies the entire data science lifecycle from data prep to machine learning to predictive model deployment. RapidMiner is a global leader in the application of machine learning in manufacturing and participates on the steering committee of Industry 4.0 in Germany.

Watch the Recording

MAPR AND RAPIDMINER

AN EXAMPLE SOLUTION FLOW FOR PREDICTIVE MAINTENANCE

Rapidminer with MapR flow diagram

CUSTOMER USING MAPR FOR PREDICTIVE MAINTENANCE

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