Predictive Maintenance: What’s the Economic Value?

The global predictive maintenance market is expected to grow to $6.3 billion by 2022, according to a report by Market Research Future. However, a new paradigm is required for analyzing real-time IoT data.

The Impact of Predictive Maintenance in Manufacturing

Predictive maintenance, which is the ability to use data-driven analytics to optimize capital equipment upkeep, is already used or will be used by 83 percent of manufacturing companies in the next two years. And it’s considered one of the most valuable applications of the Internet of Things (IoT) on the factory floor.

Benefits of Predictive Maintenance

The CXP Group report, Digital Industrial Revolution with Predictive Maintenance, revealed that 91 percent of predictive maintenance manufacturers’ see a reduction of repair time and unplanned downtime, and 93 percent see improvement of aging industrial infrastructure.

According to a PWC report, predictive maintenance in factories could:

• Reduce cost by 12 percent
• Improve uptime by 9 percent
• Reduce safety, health, environment, and quality risks by 14 percent
• Extend the lifetime of an aging asset by 20 percent

Challenges in Leveraging IoT Data for Predictive Maintenance

The CXP Group report provides examples in which, for companies like EasyJet, Transport for London (TfL), and Nestle, predictive maintenance and understanding can boost the efficiency of its technicians, benefit the customer experience, and improve unplanned downtime.

Realizing these advantages is not without challenge. Most IoT data, according to Harvard Business Review, is not currently leveraged through machine learning, squandering immense economic benefit. For example, less than one percent of unstructured data is analyzed or used at all and less than half of structure data is actively used in decision-making.

Because traditional Business Intelligence (BI) platforms were not designed to handle the plethora of IoT data streams and don’t take advantage of machine learning, use of BI reports and dashboards only periodically leads to late detection of issues.

In addition, currently, alerts are set with static thresholds, leading to false alerts in case of low thresholds, and failed detection in the instance of high thresholds. What’s more, data may change by time of day, week or season with those irregularities being outside the limited scope of static thresholds.

Last, BI platforms were not designed to correlate between the myriad of sensor data—a correlation that exponentially boosts the likelihood of detecting issues. Let’s say an engine that is about to fail may rotate faster than usual, have an unusual temperature reading and low oil level. Connecting the dots between these sensor readings through machine learning can multiply the likelihood of detection.

Automated Anomaly Detection

By addressing the deficiencies of existing BI platforms, Anodot’s automated anomaly detection paves the way for factories to realize the full value of predictive maintenance.

Analyzing big data from production floors and machinery to deliver timely alerts, Anodot’s visualizations and insights facilitate optimization, empower predictive maintenance and deliver bottom-line results.

Anodot leverages the IoT-generated stream data such as meter readings, sensor data, error events, voltage readings and more. Monitoring data over time—and in real-time—it uses machine learning to learn the metric stream’s normal behavior. It then automatically detects out-of-the-ordinary data and events, serving up diagnoses and making preemptive recommendations that represent significant cost savings on upkeep and downtime.

Rejecting old-school static thresholds, Anodot identifies anomalies in data that changes over time, for example, with built-in seasonality. This dynamic way of understanding what is happening in real time detects and alerts for real issues, often much earlier than a static threshold would have alerted. Without manual configuration, data selection or threshold settings, this platform uses machine learning to automatically calibrate to achieve the best results.

Anodot algorithms can control data of any size or complexity, such as seasonality, trends and changing behavior because they are sufficiently robust to handle an army of data variables, intelligently correlating anomalies whose connections may escape the limitations of a human observer.

Conclusion: The Future of Predictive Maintenance with Anodot

Predictive maintenance offers a hefty opportunity for factories to save money on maintenance, downtime and while extending the life of their capital equipment. Automatic anomaly detection, such as Anodot, offers the best way to expose and preempt issues that require real maintenance in real time.