Top 10 Best Predictive Maintenance Software of 2026

IBM Maximo is a comprehensive enterprise asset management (EAM) platform renowned for its advanced predictive maintenance capabilities, leveraging AI, machine learning, and IoT data to anticipate equipment failures and optimize maintenance schedules. It integrates real-time sensor data with historical records through features like Maximo Predict and Maximo Monitor, enabling proactive interventions that minimize downtime and extend asset life. Designed for complex industrial environments, Maximo provides end-to-end asset lifecycle management, from monitoring to work order automation and analytics-driven insights.

SAP Predictive Maintenance and Service is an enterprise-grade solution that uses AI, machine learning, and IoT data to predict equipment failures, optimize maintenance schedules, and enhance service operations. It integrates deeply with SAP's ecosystem, including S/4HANA and Digital Manufacturing, enabling real-time asset monitoring, anomaly detection, and automated work orders. The platform supports digital twins and scenario simulations to minimize downtime and extend asset life.

GE Digital APM is an enterprise-grade asset performance management platform tailored for heavy industries, enabling predictive maintenance through AI-driven analytics and digital twin technology. It monitors asset health in real-time using IoT data integration, predicts failures with machine learning models, and recommends optimized maintenance strategies to minimize downtime and costs. The solution supports reliability-centered maintenance across the asset lifecycle, from strategy to execution, in sectors like oil & gas, power generation, and manufacturing.

C3 AI Reliability is an enterprise AI platform specializing in predictive maintenance, using machine learning algorithms to analyze IoT sensor data, historical maintenance records, and operational metrics to predict equipment failures with high accuracy. It supports digital twins, anomaly detection, and prescriptive maintenance recommendations to optimize asset performance and minimize unplanned downtime. Designed for heavy industries like manufacturing, energy, and aerospace, it scales to manage thousands of assets across complex operations.

Aspen Mtell, from AspenTech, is an AI-driven predictive maintenance platform tailored for asset-intensive industries like oil & gas, chemicals, and power generation. It employs advanced machine learning, including unsupervised models via Mtelligence technology, to detect anomalies, predict equipment failures, and recommend optimal maintenance actions in real-time. The solution integrates with AspenONE ecosystem and plant historians to minimize unplanned downtime and extend asset life.

PTC ThingWorx is an industrial IoT platform designed for building applications that leverage real-time data from connected assets for predictive maintenance. It features advanced analytics, machine learning models, and anomaly detection to monitor equipment health, forecast failures, and optimize maintenance schedules. The platform supports scalable data ingestion from sensors, custom dashboards, and integrations with enterprise systems, making it suitable for manufacturing environments.

Uptake is an AI-driven industrial intelligence platform specializing in predictive maintenance for heavy assets in sectors like mining, energy, rail, and manufacturing. It ingests IoT sensor data, operational telemetry, and historical records to deliver failure predictions, anomaly detection, and optimization recommendations. The software enables proactive maintenance scheduling, reducing downtime and extending asset life through scalable machine learning models.

Augury is an AI-powered machine health platform designed for predictive maintenance in industrial environments, using non-invasive sensors to capture acoustic, vibration, and process data from machinery. It leverages machine learning algorithms to detect anomalies in real-time, diagnose root causes, and provide actionable recommendations to prevent failures and optimize maintenance schedules. The platform integrates seamlessly with existing operations, helping manufacturers and other heavy industries reduce unplanned downtime and extend asset lifespan.

TrendMiner is a no-code industrial analytics platform tailored for process industries, enabling engineers to analyze time-series data from sensors and historians for anomaly detection, root cause analysis, and predictive maintenance. It uses visual tools like scatter plots, trendlines, and pattern recognition to identify precursors to failures and forecast equipment health without requiring data scientists. The software integrates seamlessly with systems like OSIsoft PI, AspenTech, and others to deliver actionable insights for reducing downtime.

Fiix is a cloud-based CMMS platform that supports predictive maintenance through asset tracking, historical data analysis, and integrations with IoT sensors for condition monitoring. It helps maintenance teams shift from reactive to proactive strategies by scheduling preventive tasks, analyzing failure patterns, and generating actionable insights via dashboards and reports. While not a pure AI-driven PdM tool, Fiix excels in combining work order management with basic predictive analytics for asset health optimization.