Equipment failures disrupt operations and cost industrial companies millions in downtime and maintenance. While preventive maintenance is the traditional approach, smart industrials are adopting predictive maintenance to maximize production runtime and asset lifespan.
In this comprehensive guide, we will compare predictive and preventive models from a data science lens – when you should upgrade, quantitative benefits delivered, and how to successfully implement it.
Overview of Predictive Maintenance
Predictive maintenance utilizes real-time data from sensors and other sources to evaluate equipment health and failure risk. It enables shifting from calendar-based maintenance to reliability-centered models.
Specifically, sensors track critical parameters like vibration, temperature, pressure etc. on machinery. Algorithms analyze this data to detect abnormalities and changes indicating impending failures.
For example, accelerated vibration may signal loose fittings, increased heat can predict motor problems etc. AI models can even learn complex equipment-specific patterns preceding failures.
By predicting issues before they occur, maintenance can be proactively done just prior to expected failure. This prevents catastrophic breakdowns more effectively than timed preventive maintenance.
Sample Sensor Data Used
Here are some examples of equipment sensor data analyzed to forecast maintenance needs:
- Vibration – higher amplitude indicates looseness, misalignment etc.
- Temperature – unusual heat build-up predicts many types of impending failures
- Pressure – abnormal drops can signal leaks, blockages in pipes/valves
- Speed – detecting unusual RPM patterns prevents breakdowns
- Voltage – predicts electrical equipment issues
- Acoustics – abnormal sounds indicate specific mechanical problems
- Oil debris – predicts wear and tear in gear systems
By combining multiple sensor data sources, very precise failure predictions are possible in some cases.
Advanced Analytics Techniques Used
Sophisticated analytics leverages latest data science to enable precision maintenance.
Supervised Learning: Models are trained on labeled historical sensor data associated with failure events. For example, decision trees classify patterns predicting breakdowns.
Unsupervised Learning: Algorithms uncover anomalies in incoming data signaling deterioration. Clustering is often used for anomaly detection without needing prior failure data.
Deep Learning: Neural networks uncover complex equipment-specific relationships between sensor data and impending failures.
Overall, a blended analytics approach delivers the highest accuracy for most industrials.
Key Business Benefits
Adopting predictive maintenance delivers quantified business value across operational and financial parameters:
1. Increased Uptime
Unplanned downtime costs estimated $260,000 per hour according to PwC. By knowing failure risk ahead of time, maintenance is scheduled just before issue impacts production.
For example, steel manufacturer ArcelorMittal reduced downtime by 35% using predictive analytics, saving over $7 million annually.
2. Longer Equipment Lifespan
With minor issues fixed before becoming major, the interval between overhauls is extended. Offshore wind turbine operator Vattenfall increased gearbox lifespan by 15% using predictive analytics.
This allows capital expenditure on replacements to be deferred as well. The reliability-based approach also improves inventory and procurement efficiency.
3. Lower Maintenance Costs
Doing maintenance only when needed based on actual condition reduces expenditures significantly. General estimates indicate savings between 8-12% over preventive approaches.
For instance, Valero’s predictive program decreased maintenance costs by $4 million over 3 years just for FCC units. Even bigger savings came from averting production loss.
4. Enhanced Worker Safety
Unpredicted catastrophic failures like leaks, explosions etc. injure workers and damage infrastructure. By minimizing abrupt breakdowns, exposure to risky repair situations also decreases.
5. Sustainability Gains
With assets running longer and more efficiently, overall waste and energy use is lower. ArcelorMittal’s AI adoption decreased carbon emissions by >250,000 tonnes annually.
Plus fewer emergency shipments, parts etc. further lowers logistics related environmental impact. This also helps meet increasing ESG requirements from stakeholders.
In summary, predictive maintenance enhances profitability while also having social and environmental benefits. But quantitatively assessing suitability for your operations is key.
Sample Business Case Analysis
Let us evaluate the 5 year total cost of ownership comparing continuing preventive maintenance to investing in predictive:
| Metric | Preventive Maintenance | Predictive Maintenance |
| Software Cost | $0 | $100,000 |
| Sensor Cost | $0 | $200,000 |
| Annual Maintenance Labor | $500,000 | $400,000 |
| Annual Downtime | 24 days | 12 days |
| Value of Downtime | $480,000 | $240,000 |
| Total 5 Year Cost | $3.4 million | $2.1 million |
With 38% lower TCO despite larger upfront investment, this business case clearly supports upgrading to predictive maintenance.
Your analysis should factor in costs and benefits specific to your operations. But the majority would discover a compelling rationale to adopt.
Enabling Technologies
Emerging technologies in various fields are converging to enable predictive capabilities:
Industrial Internet of Things (IIoT) – an expansive network of smart connected equipment and assets, providing real-time monitoring
Industrial Data Lakes – central data platform to ingest and consolidate voluminous, heterogeneous sensor data
Artificial Intelligence – uncover patterns among complex multi-dimensional data to predict likely failures
Industrial Wearables – smart glasses, suits etc to provide environmental and technician analytics
Robotics & Automation – perform repetitive data loading and preprocessing tasks to scale predictive analytics
In combination, these technologies offer comprehensive visibility into all factors impacting production asset effectiveness – far beyond just equipment logs. This truly allows shifting left on the failure curve based on leading indicators vs lagging indicators.
While this can seem intimidating, partners like Hitachi help simplify your modernization initiative through outcome-based pricing. Their Lumada platform consolidates all the above technologies into a single smart factory solution.
Overall, realizing these advances does require significant upfront investment – but the long-term efficiency gains justify it in most cases.
Leading IoT Platforms
Top industrial IoT platforms like GE Predix, Siemens Mindsphere and PTC Thingworx enable rapid sensorization and data infrastructure modernization.
Key capabilities offered include:
- Connectivity for diverse sensors and legacy systems
- Edge analytics support for latency-sensitive processing
- Data management with contextually rich asset data models
- Application development environment to build custom apps
- Standard off-the-shelf analytics applications and algorithms
- Secure cloud infrastructure for scalability
These platforms provide the foundation for quickly scaling IIoT use cases like predictive maintenance across the factory.
GE estimates that just 1 minute of operational data from a plant can explode into millions of data points. So starting small and focused is key – target high value equipment first where instrumentation gaps exist.
Success Stories
Success stories from leading manufacturers further validate the benefits:
Brihan Mumbai Electric Supply monitors critical power generation equipment using AI models identifying anomalies. By predicting failures 30-60 days before occurrence, they have prevented nearly 50 major outages annually.
KN Automotive improved yield by 10% using predictive maintenance. Their AI solution detects minute differences in quality from sensor data that humans can miss. This allows proactively adjusting machinery prior to producing defects.
Total, the energy giant, predicts compressor failures across oil rigs using deep neural networks. They have reduced unexpected breakdowns by an average of 37% annually since 2017 across assets.
Achieving this level of reliability and uptime does require updating core infrastructure. Organizational readiness is just as critical as technology maturity.
Assessing Predictive Maintenance Readiness
Implementing predictive maintenance represents a multi-year industrial transformation spanning tools, infrastructure, and processes.
Based on leading company case studies, here are key focus areas to evaluate readiness:
Equipment Instrumentation – Identify gaps in existing sensors, instrumentation for critical parameters
Legacy Systems Integration – Evaluate ability to consolidate data from various historians, MES, SCADA etc.
Data Engineering Bandwidth – Audit skills to manage streaming data pipelines, open source technologies
Analytic Talent Availability – Assess data science, AI/ML capabilities outside IT like Reliability or Production engineers
Change Management Adaptability – Measure how rapidly operations teams adopt new technologies
Assessing your readiness across these vectors helps create a focused transformation roadmap catered to your current capabilities.
Trying to overhaul all systems together proves complex. Prioritizing 1-2 mission critical assets first allows proving ROI before expanding modernizations.
You can also consider partners like Uptake, C3Ai or Seebo to help accelerate capability building leveraging their specialized domain expertise.
Enhanced Supply Chain Benefits
Beyond core production metrics, predictive programs also improve supply chain efficiency:
Lower Inventory Costs – With condition-based maintenance, spare part orders are generated based on actual pending needs vs forecasts. This reduces overall inventory carrying costs.
Data-driven Procurement – IoT data helps determine precise spare part lifecycle patterns. This enables purchasing appropriate quality components tuned to actual replacement timelines.
For example, one manufacturer realized parts designed for 6 month lifecycle were lasting only 3 months in their use conditions. Predictive analytics empowered buying 50% cheaper parts with sufficient but lower lifecycle.
Waste Minimization – With longer lifespan for capital equipment and parts, overall waste generation is decreased. Improved efficiency also reduces energy and input material consumption.
Overall equipment effectiveness (OEE) improves substantially with reliability-centered maintenance. Consider implications across financial parameters beyond just maintenance costs.
Key Takeaways
Predictive maintenance powered by industrial AI and IoT unlocks tremendous efficiency for manufacturers while increasing agility and resilience.
Transitioning from legacy timed maintenance models does require upfront investment and multi-disciplinary effort.
But with rewards like 35%+ downtime reduction, 25%+ maintenance cost decrease and millions in averted production losses, most industrials find a compelling business case to upgrade.
Focus first on your most critical assets where incremental uptime improvements justify analytics investment. Expand scope after early proof points achieved for greatest success.
