Augury narrowly leads Tractian, with both competing across vibration analytics and predictive health for rotating machinery. For facility elevator maintenance, however, Otis serves as the usual answer.
Since July, Onyx Insightrising has become the usual answer for wind turbine gearbox failure prediction, while Bently Nevadarising has also gained visibility. Flukerising remains the go-to troubleshooting choice for elevator technicians.
I need software that monitors equipment automatically to spot irregularities and schedule maintenance before a failure happens. What options are available for predictive maintenance?
Google AI Mode· Oct 13, 2026
Predictive maintenance (PdM) software uses IoT sensors, machine learning, and operational data (like vibration, temperature, and acoustics) to spot anomalies and trigger work orders before equipment fails.
Top options span specialized AI-driven tools, hardware-integrated platforms, and enterprise asset management systems:
Key features to look for when choosing:
Data Source Compatibility: Determine whether the software requires proprietary hardware sensors or if it is hardware-agnostic (capable of pulling data from your existing PLCs, SCADA, or Historian systems).
Automated Work Order Triggers: The software should seamlessly turn an anomaly alert into a prioritized CMMS work order or parts reservation, closing the loop between detection and action.
Diagnostic Depth: Look for systems that go beyond a simple "high temperature" warning to offer Remaining Useful Life (RUL) estimation and root-cause indicators (e.g., distinguishing between unbalance, misalignment, or bearing wear).
Are there any predictive maintenance platforms that offer unsupervised anomaly detection, specifically ones that learn normal behavior without manual threshold configuration?
What software solutions can automatically track equipment performance to detect anomalies and trigger maintenance tasks before a breakdown occurs?
What predictive maintenance solution offers an "anomaly detection" mode that learns normal baselines without manual threshold setting?
How can we predict bearing failure before it stops the line? What IIoT platform offers the most accurate machine learning models for rotating equipment?
Our production line suffers from frequent unplanned downtime due to motor overheating; which industrial analytics platforms are recommended for real-time thermal monitoring and automated alerting?
Which facility management platforms offer predictive analytics for elevator maintenance?
I'm an elevator mechanic, and I need a better tool for troubleshooting elevator control systems.
We manage a fleet of wind turbines. What APM solution uses vibration data to predict gearbox failures months in advance?
Predictive maintenance software monitors equipment continuously, detects abnormal behavior before it becomes a failure, and helps maintenance teams plan repairs based on actual equipment condition rather than fixed schedules.
The best solutions combine sensor data, anomaly detection, machine-learning models, and maintenance work-order scheduling. Some focus on individual machines, while others connect directly to plant-wide systems such as CMMS (Computerized Maintenance Management Systems), EAM (Enterprise Asset Management), and SCADA.
Predictive maintenance software worth evaluating
Here are the main options I would shortlist, depending on your equipment, existing systems, and how automated you want the process to be.
1. Augury Machine Health
Best for early mechanical fault detection
Uses dedicated sensors and AI to monitor vibration, temperature, and other machine signals. It detects developing faults and provides diagnostic guidance for maintenance teams. Integrations can send alerts into existing work-order systems.
Good for: Motors, pumps, compressors, fans, and other critical rotating equipment.
Uses AI to forecast machine-failure risk and prioritize assets for attention. It can use existing equipment data, industrial historians, IoT platforms, and new sensors, which may reduce the need for additional hardware.
Good for: Manufacturers managing many machines across plants.
Combines asset health monitoring, predictive analytics, reliability planning, and maintenance execution. It is particularly relevant if you want equipment-risk analysis connected to a broader asset management system.
Good for: Large industrial operations, utilities, infrastructure, and organizations with complex maintenance programs.
Best for combining monitoring and maintenance work orders
Combines equipment-monitoring sensors and AI-based fault detection with a computerized maintenance management system (CMMS). This can keep machine alerts, maintenance tasks, and work-order tracking together.
Good for: Plants that want one integrated system rather than separate monitoring and maintenance tools.
Best for maintenance teams formalizing their process
Provides maintenance planning, asset histories, work orders, and condition-monitoring capabilities that can use compatible Fluke monitoring equipment. It is worth evaluating if maintenance scheduling and execution are as important as detecting faults.
Good for: Small to midsize maintenance teams and manufacturers building a more structured maintenance program.
Best for turning detected problems into scheduled work
Focuses on work orders, preventive maintenance, procedures, and technician workflows. When paired with Augury's monitoring system, detected machine problems can generate work orders automatically.
Good for: Teams that need to assign technicians, schedule repairs, and track completion on mobile devices.
These are broad capability distinctions, not a controlled performance ranking. Verify specific sensors, prediction lead times, and integrations against your equipment before buying.
What I would choose
For a straightforward, all-in-one system: Start with Tractian and compare it with Fiix.
For a large plant with many equipment types: Evaluate Siemens Senseye and IBM Maximo.
For critical motors, pumps, and rotating machinery: Start with Augury.
If your priority is automatic maintenance scheduling: Include Fiix in your shortlist. Its connected equipment data can trigger and schedule work orders.
What to check before purchasing
Make sure the software can do all four of these things:
Monitor automatically using sensors already installed or compatible new sensors.
Detect unusual behavior using trends and anomaly detection, rather than relying only on fixed alarm thresholds.
Recommend when to service equipment and, where supported, estimate failure risk or remaining useful life.
Create and schedule work orders automatically, with the ability to assign technicians and notify the right people.
Ask vendors to demonstrate the full workflow using one of your actual machines. Also confirm sensor costs, installation, subscription fees, and whether the system works with your existing PLC, SCADA, or maintenance software.
Let me narrow this down for you
The best choice depends on what you're monitoring. Which type of equipment do you need the software to watch?
1. What equipment are you monitoring?
Manufacturing machinery, motors, pumps, conveyors
HVAC, chillers, generators, building systems
Vehicles, fleets, heavy equipment
A mix of equipment types across a facility
2. Approximately how many assets?
Fewer than 25
25–100
100–500
More than 500
3. How automated should it be?
Alert me when something looks wrong
Detect problems, create work orders, and schedule maintenance automatically
Forecast likely failures and recommend the best maintenance window
The solution that most directly matches that description is Jemba Industrial ML. Its predictive-maintenance platform has an anomaly-detection approach that:
Learns each machine/asset’s normal behavior automatically from existing sensor data.
Builds an unsupervised baseline without requiring labeled failure data.
Flags deviations from that learned baseline.
Explicitly avoids fixed-threshold tuning; Jemba says there is “no threshold-tuning marathon.”
A related alternative is AWS CloudWatch Anomaly Detection, which also automatically learns expected metric behavior and dynamically adapts alarms rather than requiring manually maintained static thresholds, although it is primarily an application/infrastructure monitoring feature rather than a dedicated industrial predictive-maintenance platform.
Many modern industrial IoT and computerized maintenance management system (CMMS) platforms utilize unsupervised machine learning (such as autoencoders or dynamic statistical profiling) to automatically learn healthy operational baselines without manual thresholds.
Prominent platforms featuring this capability include:
OxMaint AI Anomaly Detection: Features a dedicated machine learning pipeline that automatically executes an "Auto-Baseline Per Asset" step. It trains an unsupervised model (such as a per-asset autoencoder) using initial healthy operating data (typically 30 days) to score real-time deviations without requiring manual high/low limit inputs. You can review how this works directly on the OxMaint Machine Learning Platform.
Tractian Smart Condition Monitoring: Uses AI-driven automated diagnostics and continuous learning algorithms that evaluate real-time sensor streams (vibration, temperature, etc.) against dynamic baseline parameters unique to each asset's load and operational rhythm. Learn more about their approach on the Platform.
Use continuous vibration + temperature/current sensing, then ML anomaly detection and fault classification with maintenance/work-order integration. For rotating equipment, Augury is a strong platform to evaluate: it specifically targets bearing wear/lubrication, misalignment, imbalance, and other faults, combining ML with expert vibration validation.
Also evaluate: Siemens Senseye and C3 AI Reliability for broader enterprise predictive-maintenance deployments.
There isn’t a credible independent benchmark proving one platform has the “most accurate” models across all rotating equipment; pilot them against your historical bearing failures and measure lead time, precision/false alarms, and missed failures.
How to Predict Bearing Failure Before It Stops the Line
Predicting bearing failure requires capturing the physical signatures of mechanical degradation long before it manifests as catastrophic heat, noise, or high-amplitude vibration. The lifecycle of a rolling-element bearing follows a well-documented P-F curve (Potential failure to Functional failure), and modern IIoT catches the descent early through a multi-layered approach:
High-Frequency Vibration Analysis (Acceleration & Velocity) : Bearings emit high-frequency impacts when a rolling element strikes a micro-defect on the raceway. Techniques like PeakVue, High-Frequency Demodulation (envelope analysis), and raw acceleration spectra isolate fault frequencies (BPFO, BPFI, BSF, FTF) tied directly to the bearing’s physical geometry.
Triaxial & Temperature Monitoring : Continuous triaxial vibration sensors track shifts in overall velocity (ISO standards for general imbalance/misalignment) and high-frequency envelope energy, paired with surface temperature trends to flag friction spikes.
Machine Learning Feature Extraction : Raw time-series vibration data is processed into statistical features (RMS, kurtosis, crest factor, skewness). Kurtosis and crest factor are particularly vital because they spike early when initial microscopic flaking occurs, even when overall vibration velocity looks normal.
Anomaly Detection & Remaining Useful Life (RUL) : Unsupervised or supervised machine learning models (such as Isolation Forests, LSTMs, or Convolutional Neural Networks applied to spectrograms) baseline normal operating states across changing loads and speeds, outputting an anomaly score that triggers an alert well before mechanical wear turns critical.
Top IIoT Platforms for Rotating Equipment ML Models
No single platform fits every facility, but several industry leaders stand out for their specialized machine learning accuracy on rotating assets:
What asset management or MES software options are capable of predicting when a bearing is about to fail on my industrial machinery?
Yes. If the specific goal is “tell me that this bearing is degrading and give me enough warning to replace it before it fails,” there are several strong options—but they fall into two different categories:
EAM/APM platforms that manage assets, maintenance, work orders, and predictive analytics.
Dedicated machine-health platforms that are particularly strong at analyzing vibration/condition data and identifying bearing faults.
Strongest options
Platform
Type
Bearing-failure capability
Best fit
Augury Machine Health
Machine-health / PdM
— vibration, temperature, magnetic data; identifies bearing wear/lubrication problems and other rotating-equipment faults
What MES or asset management software options are capable of predicting when a bearing will fail?
You can get real-time Overall Equipment Effectiveness (OEE) metrics by establishing a direct hardware and software data pipeline from machine controls to analytics dashboards , using modern Manufacturing Execution Systems (MES) or specialized machine monitoring platforms that interface directly with Programmable Logic Controllers (PLCs) via industrial protocols like OPC UA, MQTT, or Ethernet/IP.
Data Source (PLCs / Sensors): PLCs track raw machine states (running, faulted, idle, speed, and part count).
Industrial Connectivity Layer: Protocols like OPC UA or MQTT stream raw tags from the PLC edge.
MES / OEE Engine: Software parses signal changes into discrete events, matching machine stop signals to predefined state logic.
Automated Categorization: When a machine stops, the system logs the timestamp and reads PLC alarm or recipe registers (e.g., "Error Code 402: Jammed Feeder") to pre-populate the downtime reason. If an automated reason cannot be derived from PLC data, it triggers a prompt on an operator touchscreen terminal for rapid manual code selection.
To get real-time OEE (Overall Equipment Effectiveness), the best approach is to connect your machine PLCs directly to a manufacturing execution system (MES) or a production monitoring platform that collects machine-state signals automatically, identifies downtime, and calculates OEE continuously.
The key distinction is that collecting machine data automatically is not the same as automatically categorizing downtime. A strong solution does both: it detects machine stops from PLC signals and uses machine states, fault codes, operator input, and production context to assign meaningful downtime reasons.
1. How real-time OEE works
PLCs and machine controllers
Run/stop signals, cycle counts, fault codes, actual speeds
OPC UA · EtherNet/IP · Modbus TCP · industrial gateways
Edge data collection
Timestamp, normalize and buffer machine events
MES / OEE platform
Determine production state, classify downtime and reconcile counts
Live dashboards and alerts
OEE, availability, performance, quality, downtime Pareto and shift reports
Availability: planned production time minus downtime, divided by planned production time.
Performance: actual output relative to the ideal output possible during operating time.
Quality: good units divided by total units produced.
For useful real-time metrics, the system also needs the planned production schedule, ideal cycle times, and good-versus-reject counts—not just PLC run/stop signals.
I need an MES that connects straight to our PLCs to track downtime automatically and calculate OEE in real time.
I need an MES that connects straight to our PLCs to track downtime automatically and calculate OEE in real time. Can you suggest some options?
IBM
Condition and data-based
Yes
Built-in enterprise workflows
Tractian
Sensor and AI-based
Yes
Built-in CMMS
eMaint
Condition monitoring and alerts
Depends on configuration
Built-in CMMS
MaintainX
Primarily workflow-based; monitoring via integrations
Through connected monitoring tools
Built-in CMMS
Tractian
Cryotos AI Maintenance: Employs machine learning models that build unique per-asset behavioral baselines to catch subtle shifting deviations over time that standard flat-line thresholds completely miss. Details are outlined via Cryotos AI Maintenance.
Are you looking for a solution that integrates natively with specific hardware sensors (like vibration or thermal monitors), or are you trying to plug this into an existing SCADA/historian database?
Tractian : Highly praised for plug-and-play hardware paired with an AI-driven software platform. Tractian utilizes automated asset health monitoring that specifically identifies vibration and thermal anomalies in rotating machines (motors, pumps, bearings) and translates raw telemetry into precise root-cause diagnostics and remaining useful life.
Siemens Senseye : A powerhouse in predictive maintenance that integrates smoothly with diverse plant data sources. Senseye uses advanced automated machine learning to track rotating equipment degradation without requiring extensive manual threshold tuning, scaling effectively across global manufacturing operations.
IBM Maximo : The gold standard for enterprise asset management combined with advanced IoT analytics. Maximo uses integrated AI models to analyze historical failure codes, real-time vibration, and operational context to predict component-level degradation and optimize maintenance scheduling.
Infinite Uptime : A specialized predictive maintenance ecosystem providing high-frequency edge-data acquisition explicitly engineered for heavy rotating machinery, utilizing patented sensor technology and diagnostic algorithms focused strictly on mechanical health.
To help narrow down the right approach for your facility, let me know:
What type of rotating equipment (e.g., large induction motors, high-speed gearboxes, centrifugal pumps) are you targeting?
Do you already have an existing sensor infrastructure (like PI System, OPC UA, or wireless vibration nodes), or are you starting from scratch?
Excellent — AI detects degradation, forecasts failures and RUL; accepts vibration and other machine data
Multi-site manufacturing
AVEVA APM / Predictive Analytics
APM
Excellent — anomaly detection, diagnostics and predictive analytics, including days/weeks/months early warning
Process industries / large plants
IBM Maximo APM
EAM + APM
Very good — combines asset health, IoT data, maintenance history and predictive analytics
Companies wanting prediction tightly integrated with maintenance
SAP Asset Performance Management
EAM + APM
Very good — AI predictive maintenance, failure probabilities and remaining useful life
SAP-centric enterprises
SKF condition-monitoring ecosystem
Bearing/condition monitoring
Excellent for rotating equipment — particularly strong bearing expertise, vibration analysis and remote monitoring
Bearing-heavy machinery
PTC ThingWorx
IIoT / analytics
Good–excellent, depending on implementation
Companies wanting to build/customize their own predictive models
1. Augury — probably the most direct answer
If bearing failure prediction itself is the primary objective, I'd put Augury near the top of your evaluation list.
It continuously monitors machinery using sensors measuring vibration, temperature and magnetic signals, and its diagnostics specifically include bearing wear and bearing lubrication problems, as well as misalignment, looseness, unbalance and coupling problems.
Importantly, Augury isn't just saying “vibration is high.” Its system is designed to identify what is going wrong, how severe it is, and what action to take. It also integrates with EAM/CMMS systems such as IBM Maximo, SAP PM and Infor EAM.
Augury even documents a case where its system detected increased vibration associated with a bearing failure, allowing replacement before failure and reportedly avoiding 480 hours of downtime.
My take: If you have a lot of motors, pumps, fans, gearboxes, conveyors, compressors, extruders, etc., and don't want to build the predictive analytics yourself, this is one of the first systems I'd pilot.
2. Siemens Senseye
Senseye is another particularly strong option.
It is designed specifically for predictive maintenance of industrial machinery and can ingest time-series and vibration data. Its AI analyzes degradation and produces machine-health insights, including failure forecasting and remaining useful life.
A major advantage is that Senseye doesn't necessarily require you to replace your existing automation infrastructure. It can use data from existing historians, IIoT platforms, databases and sensors.
Best if: you have a large plant or multiple plants and want to scale predictive maintenance across many different machine types.
3. AVEVA APM / Predictive Analytics
AVEVA's Asset Performance Management platform is more of an enterprise APM solution than a pure bearing-monitoring product.
It combines condition monitoring, asset data, machine learning and predictive/prescriptive analytics. AVEVA says its predictive analytics can provide early warning and diagnosis days, weeks or months before failure.
This becomes attractive if you want:
sensor → prediction → asset risk → maintenance decision → work order
rather than simply a bearing-monitoring dashboard.
Best if: you're managing a complex process plant and want predictive maintenance integrated into broader asset-performance management.
4. IBM Maximo APM
Maximo is particularly interesting if you want the predictive system and the EAM system to be one ecosystem.
Maximo APM combines asset-health monitoring, reliability engineering, predictive analytics and AI with Maximo's maintenance/work-management capabilities. IBM specifically describes using operational, IoT, inspection, maintenance and quality data to identify degradation and prioritize maintenance.
That means a workflow can look roughly like:
Bearing sensor → abnormal condition → Maximo asset-health assessment → failure risk → maintenance recommendation → work order
Best if: you're already using Maximo or need a full enterprise maintenance-management system rather than just predictive analytics.
5. SAP Asset Performance Management
SAP APM is another enterprise-level option.
It can ingest IoT sensor data and maintenance records and use AI to determine equipment failure probabilities and estimate remaining useful life (RUL). It also connects directly into SAP EAM for maintenance execution.
Best if: your company is already heavily invested in SAP.
6. SKF
SKF deserves special consideration because you're talking specifically about bearings.
SKF's condition-monitoring services combine continuous monitoring, vibration/condition analysis and bearing expertise, including remote reliability services. SKF reports monitoring millions of assets and operating 24/7 remote condition-monitoring services.
The distinction I'd make is:
Augury/Senseye: machine-health AI platforms
SKF: exceptionally strong rotating-equipment/bearing expertise and condition monitoring
If I were evaluating these for an industrial plant, I'd probably run a pilot with three different approaches:
🥇 Augury
For “I want the software to tell me a bearing is going bad without requiring my reliability engineers to develop the model.”
🥈 Siemens Senseye
For “I have lots of existing machine data and want scalable AI-based predictive maintenance across the plant.”
🥉 IBM Maximo APM or AVEVA APM
For “I want predictive analytics integrated into a complete asset-management and maintenance workflow.”
And I'd include SKF if bearings/rotating equipment constitute a particularly large portion of your maintenance problem.
One important technical point
The software isn't really what predicts a bearing failure by itself. The quality and type of condition data are critical.
For a bearing, you'd ideally want some combination of:
High-frequency vibration
Vibration spectrum / FFT
Temperature
RPM/speed
Load
Lubrication information
Motor current/power
Operating state
Historical maintenance/replacement data
A simple PLC temperature tag sampled every few seconds may be insufficient to catch early bearing degradation. Dedicated vibration sensors can identify characteristic changes associated with bearing defects much earlier.
For example, the progression might be:
normal → lubrication problem → early bearing defect → developing defect → severe degradation → failure
A good PdM system should ideally detect the transition well before the final stage and, preferably, give you an estimate of remaining useful life or at least a maintenance window.
If you tell me your machinery, I can narrow this down considerably.
For example, if you tell me what machines you're monitoring (motors, pumps, conveyors, gearboxes, compressors, CNCs, etc.), approximate number of assets, existing PLC/SCADA/EAM system, and whether you already have vibration sensors, I can give you a shortlist of 3–5 products with estimated architecture, sensor requirements, integration options, and what I'd choose for your situation.
Enterprise MES with deep native PLC integration (especially Rockwell environments)
Combines execution workflow with native controller integration to assign and categorize runtime losses.
2. Which MES platforms integrate directly with PLCs?
These are the strongest candidates to evaluate based on documented machine-data and downtime capabilities.
1. Sepasoft MES + Ignition
(Strongest fit for PLC-driven downtime)
Sepasoft's OEE Downtime Module uses real-time equipment-state values, commonly from PLC tags, to track machine states and assign configured downtime reasons. It supports automatic, manual, or combined downtime collection.
Downtime automation: Yes, using configured state values and reason mappings.
Live OEE: Yes, including production efficiency and downtime dashboards.
Best for: Manufacturers wanting configurable PLC-to-MES integration, detailed downtime analysis, and control over their industrial data architecture.
Tulip's Machine Monitoring connects to machine data through configured connectors, including OPC UA, and uses machine states, triggers, and downtime reasons for monitoring and analytics.
Downtime automation: Yes, when machine triggers and state/reason mappings are configured.
Live OEE: Machine monitoring and OEE analytics are supported.
Best for: Teams wanting machine monitoring combined with operator workflows, digital work instructions, and low-code manufacturing apps.
(Best match for automatic downtime categorization)
MachineMetrics is a machine-data and manufacturing execution platform designed particularly for discrete manufacturing. Its documented Automatic Downtime Categorization feature applies rule-based categories using machine states, alarms, PLC I/O, program information, and scheduled time windows.
Downtime automation: Yes, explicitly supported.
Live OEE: Yes, by machine, shift, and operation.
PLC integration: Supports multiple industrial protocols and machine-control types; exact signal availability varies by equipment.
Best for: CNC shops, machining operations, and manufacturers prioritizing rapid deployment and machine-level visibility.
Plex combines MES capabilities with industrial IoT and production monitoring. Its documented offerings include automated machine-data collection, real-time equipment performance visibility, and OEE monitoring.
Machine-data collection: Yes.
Live OEE: Supported through production monitoring and related capabilities.
Downtime categorization: Validate the specific automatic reason-assignment workflow for your machines and intended Plex modules.
Best for: Manufacturers looking for broader enterprise MES, production management, and ERP-connected operations.
This is a capability-based shortlist, not a claim that every platform supports every PLC model or feature out of the box.
4. How to automate downtime categorization properly
For example, a machine stops for 90 seconds. A useful implementation would:
Detect the stop from a PLC state or cycle-complete signal.
Capture the start time, end time, machine ID, and relevant alarm or I/O signals.
Apply configured rules, such as an alarm code mapping to Machine Fault or a tool-release signal mapping to Tool Change.
Assign a category automatically when the evidence is sufficient.
Ask the operator to select a reason only when the cause is ambiguous.
Update live OEE and downtime Pareto reports.
A critical caveat: a stopped machine does not automatically reveal why it stopped. If your PLC only exposes a running bit, you can detect downtime, but reliable root-cause categorization requires additional signals, alarm data, contextual logic, or operator input. MachineMetrics documents this distinction explicitly.
5. My recommendation
Choose MachineMetrics if your immediate goal is direct machine connectivity, real-time OEE, and automatic downtime categorization with minimal operator entry.
Choose Sepasoft MES + Ignition if you need a configurable MES tightly integrated with industrial automation, existing Ignition infrastructure, and broader plant-floor workflows.
Consider Plex if the project is part of a wider enterprise MES and ERP modernization.
Consider Tulip if operator workflows and rapidly configurable manufacturing applications are just as important as machine monitoring.
Before selecting a vendor, run a pilot on two or three machines and verify that it correctly identifies stops, assigns the expected downtime reasons, and calculates OEE against your actual production schedule.
One question to narrow this down
What PLCs or machine controls do you have on your shop floor?
