AI for Predictive Maintenance
Prevent critical failures while increasing throughput with AI for predictive maintenance
Maintain your assets based on their real-time condition, not what the calendar says. Preventative maintenance often leads to unnecessary operating costs, whether from excessively frequent maintenance, or unexpected asset failures between scheduled maintenance that cause costly downtime and diminish your asset’s lifespan. Learn how SparkCognition solves this problem using AI for predictive maintenance to unlock insights inside your data that warn you of critical failures before it’s too late.
What is predictive maintenance?
Predictive maintenance leverages real-time data collected from your deployed assets and the environment in which they operate to learn and recommend the best possible time to perform maintenance while warning of emergent failures with sufficient advance notice so you can prevent unexpected downtime.
Predictive maintenance in oil and gas
Predictive maintenance in manufacturing
Predictive maintenance in aviation
How does AI enable predictive maintenance?
AI for predictive maintenance uses machine learning algorithms to ingest historical sensor data from a facility’s operations. This data is then used to build a model trained to recognize what normal operations look like. The normal behavior model can then analyze sensor data in real time and identify and flag operational conditions that deviate from the established norm. Adding in historical sensor data and knowledge from subject matter experts (SMEs), the model learns to send alerts of impending failures before they ever occur, rather than simply warning that a component is at risk.
Prevent critical failures and unplanned downtime
Predict downtime and failure events days in advance. Prevent costly interruptions that cause ripple effects throughout your operation. AI for predictive maintenance can provide notice of pending asset failures up to weeks in advance, reducing risk exposure to workers and preserving uptime.
Increase asset availability and production throughput
Increase throughput by keeping assets performing at their peak capacity. AI for predictive maintenance unlocks new modes of visibility into complex operations, identifying precursors to system failures.
Reduce maintenance costs and optimize spare inventory
Reduce costs by maintaining equipment based on condition, performing the right actions, and planning in advance. AI for predictive maintenance helps you minimize inventory and redundant equipment service.
Provide operational visibility and asset insights
Get end-to-end visibility and deeper insights into asset performance and machine health, allowing for more flexibility and increased operational efficiency.
AI predictive maintenance solutions across industries
AI-powered predictive maintenance in manufacturing
Reduce downtime, micro stoppages, and slowdowns with AI predictive maintenance for manufacturing.
- Increase throughput and prevent quality issues.
- Improve operational efficiency.
- Reduce maintenance costs.
AI-powered predictive maintenance in oil and gas
- Avoid unplanned critical failures that can cost millions.
- Reduce maintenance costs and inventory and extend asset life.
- Empower your team with asset insights.
AI-powered predictive maintenance in renewables
- Keep your assets generating optimal energy.
- Prevent failure events that can cost up to $150k.
- Gain maintenance efficiencies on remote assets.
- Optimize spare inventory and minimize expensive repairs.
AI-powered predictive maintenance in power and utilities
Prevent failures, extend asset life, and optimize your maintenance with AI predictive maintenance for power and utilities.
- Prevent failure of critical assets.
- Reduce maintenance costs.
- Extend the useful life of capital intensive assets.
How SparkCognition delivers AI predictive maintenance
SparkCognition’s AI for predictive maintenance solutions applies machine learning algorithms to historical sensor data from assets to build a baseline model of what normal operations look like. The normal behavior model is then used to analyze asset sensor data in real time, identifying and flagging any values that deviate from the established norm.
Deviating values indicate anomalous behaviors that are likely to precede an impending failure event. Not only does this enable you to monitor the overall health of your asset in the context of day-to-day operation, but it also gives your SMEs ample time to proactively address and prevent disruptive events from occurring.
STEP 1: DATA INGESTION
Initial stages typically revolve around real-time data integration, using proprietary data ingestion connectors to tap into existing data sources and securely pull real-time data.
STEP 2: TRAINING DATA
After subjecting the data to basic quality validation and cleaning, deep learning models detect anomalies in resource consumption, asset health, and overall process efficiency.
STEP 3: MODEL DEVELOPMENT
Next, models are tailored to suit the local context for real-time model execution and KPI-driven reporting. As more data is ingested over time, the solution predicts future problems based on historical trends it has detected.