AI predictive maintenance tools for industry

Artesis sells e-MCM for industrial teams monitoring motors and the equipment they drive. It learns each motor’s electrical behavior from current and voltage measured at the motor control cabinet. The system flags changes involving the stator, rotor, bearings, mechanical components, power supply and driven equipment. It connects to plant systems through Modbus and can feed OmniSight in the cloud or on premises.

The installation uses one unit per motor, with potential and current transformers needed for medium voltage equipment. It needs a learning period across normal operating points before reporting findings. After an overhaul, the system relearns the repaired machine as its reference.

There are explicit diagnostic boundaries. e-MCM reports fault categories, with inspection or testing needed to confirm the specific cause. It does not analyze captures during large load swings. Early insulation changes, open circuits, phase to ground faults and shorted laminations require an offline test. Installation requires three phase voltage and current, control power, nameplate data and network access for plant systems. Bearing specific markers need the bearing model, and pump performance analysis needs the pump datasheet. The unit provides local status and relay alarms.

Emerson wholly owns AspenTech, whose Aspen Mtell software predicts equipment failures and recommends maintenance for industrial operators. The acquisition completed in March 2025. Mtell uses AI failure prediction and embedded failure mode and effects analysis to identify developing problems and prescribe corrective actions.

The product has asset templates for deployment and integrations that deliver findings into enterprise asset management and ERP workflows. It also integrates with Emerson AMS vibration monitoring. Customers include YPF’s refining and upstream operations. Its embedded analysis maps failure modes to recommended corrective maintenance actions.

AssetWatch sells condition monitoring for industrial plants that combines AI analysis with reliability engineers. Its platform brings together vibration, temperature, electrical and oil analysis data. A certified expert validates findings before they reach the maintenance team, with each alert describing the fault and recommended action.

The company offers a $199 introductory trial and describes its production service as an inclusive subscription without a capital equipment purchase. Accounts receive a condition monitoring engineer, an asset reliability technician and a customer success manager. AssetWatch raised $75 million in a Series C led by Viking Global Investors in April 2025.

Augury’s Machine Health uses AI diagnostics and expert guidance to identify developing equipment faults for manufacturing maintenance teams. Its industrial offering covers motors, pumps and compressors, along with bearings, gearboxes, fans and other production equipment. Findings identify what is failing, its urgency and the action to take.

Avathon sells asset management AI for operators of capital intensive equipment, including energy, mining, aerospace and manufacturing businesses. It connects sensor histories, maintenance records, manuals and asset relationships in a knowledge graph. Models learn normal behavior and score failure probability by equipment class, while agents trace anomalies through those relationships.

The system turns predictions into work orders that include parts, crew and a maintenance window, then feeds the outcome back into its models. Avathon offers a 45 minute engineering session using the prospective customer’s asset hierarchy and a sample of real maintenance history.

Fluke Reliability owns Azima DLI, which supplies vibration analytics and remote condition monitoring for industrial maintenance teams. Its Watchman 360 platform detects specific machinery faults, predicts time to failure and recommends corrective actions. The service combines automated analysis with specialists who handle complex vibration diagnosis and provide maintenance reports.

Guidewheel uses clip on electrical sensors to monitor factory machinery and applies AI anomaly detection to identify problems and maintenance needs. Its FactoryOps software also tracks machine utilization, production cycles, downtime and energy consumption. Operators can record downtime reasons and request support through a desktop or mobile dashboard.

Guidewheel prices per machine and site, with a custom quote for each operation. The package includes deployment infrastructure and connects to the cloud through Ethernet, WiFi or LTE. It integrates with ERP and CMMS systems through native connections, Workato and an open API; customers receive onboarding and ongoing support.

I-care combines Wi-care sensors and I-see software with reliability services for industrial maintenance teams. Its AI categorizes readings as healthy operation, a potential issue or a critical alarm. Analysts review the generated reports, and the software suggests default recommendations to support their diagnosis.

I-care also sells maintenance as a service, bundling hardware, software and expert support. The platform connects to CMMS systems and monitors the health of its own sensors, transmitters and gateways. The company completed a $23.2 million financing and refinancing in December 2025, reserved for existing shareholders and employees.

Infinite Uptime sells prescriptive maintenance for heavy manufacturing through its PlantOS platform. It combines mechanical and process signals to identify developing equipment faults and name the corrective action. Its target industries include steel, cement, mining, chemicals, pulp and paper, and food production.

PlantOS uses existing PLC and DCS data alongside vibration and temperature measurements. Its service distinguishes equipment specific AI models for complex production assets from continuous and periodic monitoring for other machinery. Infinite Uptime describes its commercial model as Production Outcomes as a Service, with payment tied to outcomes. Operators digitally sign off prescriptions, returning feedback into the models.

KCF Technologies sells AI machine health monitoring for industrial plants through SMARTdiagnostics software, sensors and reliability services. Sensors measure vibration, temperature and other equipment conditions, while the software detects faults and guides maintenance. KCF describes itself as private and employee owned.

Its $199 trial lasts 30 days and includes 48 vibration sensors and a base station. Production pricing is customized, with both capital purchase and subscription models available.

The three service tiers differ in who reviews the findings. DeskAI leaves the customer’s own reliability team to manage automated fault alerts. DeskAI+ adds human validation by KCF. SENTRYservices adds a dedicated analyst who helps verify faults, investigate root causes, configure the system and collaborate with the plant team. Customers who do not proceed after the trial return the hardware. Its hardware options vary by asset: the IoT HUB supports high temperature locations, third party sensors and PLC triggered collection. Piezo sensing is offered for highly critical machinery and equipment running at very high or low speeds.

KONUX sells predictive maintenance for railway switches. KONUX Switch combines industrial IoT devices, AI and railway engineering knowledge to identify developing failures, diagnose problems and plan maintenance. The company also offers modular rail sensors and the CoBrix data platform for railway stakeholders.

MachineMetrics uses machine learning anomaly detection to warn manufacturing teams when equipment or cutting tools behave abnormally. It also provides machine health timelines, diagnostics and alerts. Connections to MaintainX, Fiix and UpKeep can automate maintenance work orders from machine data.

The company sells three subscription plans: Core Platform, Intelligent MES and Enterprise. Pricing is based on machine volume, with a lower per machine cost as the deployment grows. All plans include onboarding, training and remote technical support. The system is hosted in the cloud; older equipment or particular use cases can require extra gateways, sensors or interfaces.

Nanoprecise sells AI based predictive maintenance for industrial equipment using MachineDoctor sensors and diagnostic software. Its sensors connect through cellular networks or WiFi, and the service provides equipment health insights and guidance on maintenance priorities. The company lists Petronas and Tata Steel among the businesses that use its technology.

Nanoprecise announced a $38 million Series C in March 2025 that combined equity and debt. Yaletown Partners led the equity financing, BDC’s Industrial Innovation Venture Fund co-led, and CIBC Innovation Banking supplied a credit facility. Its offering also links maintenance with energy efficiency, tracking equipment condition alongside its energy centered maintenance approach.

Petasense combines wireless sensors and cloud AI for industrial teams monitoring rotating machines and other equipment. Its Asset Reliability & Optimization Cloud ingests sensor data, models assets with digital twins and detects failure modes. Teams receive alerts and dashboards through web and mobile applications, with connections to process historians and CMMS systems.

Samotics sells SAM4 for monitoring motor driven equipment from the electrical cabinet. Hardware or a compatible ABB drive supplies electrical measurements, which the analytics use to detect developing fault patterns. Samotics reviews findings and recommends next steps, while the customer sees the evidence, likely fault and urgency in a dashboard.

Its customer examples include Yorkshire Water and Nyrstar, covering wastewater pumps and zinc smelter equipment. The cabinet installation supports monitoring submerged pumps and assets in harsh environments where mounting vibration sensors is difficult. SAM4’s deployment uses cabinet hardware or compatible drives, and the customer chooses whether to inspect, schedule maintenance or continue monitoring after a finding.

Siemens owns Senseye, whose Cloud Application forecasts equipment failures and prioritizes maintenance risks for manufacturers. The product uses existing machine data from historians, IoT platforms, databases or sensors. AI models machine and maintainer behavior, estimates remaining useful life and identifies assets needing attention across multiple plants.

Siemens sells Senseye through a custom sales quote. New sensors are optional when suitable condition and operational data already exist. Siemens provides implementation and scaling support, and names BlueScope Steel as a customer. Maintenance knowledge and failure patterns are stored in a shared platform for use across teams and sites.

Shift5 turns onboard serial bus data into maintenance insights for defense and transportation fleets. It gives maintenance teams access to operating data and early warnings during live missions. The platform can share alerts with existing systems for reporting, trend analysis and investigation of failures across a fleet.

SymphonyAI offers Predictive Asset Intelligence for industrial operators. Its APM 360 software uses machine learning to identify anomalies in machinery behavior while considering manufacturing process context. The cloud service covers asset health, condition monitoring and predictive maintenance, and recommends actions for maintenance teams.

Tractian sells AI equipment health monitoring for manufacturers, utilities, data centers and other operators of critical machinery. It combines vibration, ultrasound and other sensor signals to diagnose faults, identify root causes and recommend action. The company announced a $130 million Series D in October 2026, led by Sapphire Ventures and co-led by M&G Catalyst.

Tractian uses custom asset based pricing. A specialist reviews critical equipment, likely failure modes and required monitoring coverage, with an on site assessment where useful. The resulting plan is priced around asset risk and coverage rather than a simple count of sensors.

Treon combines wireless sensing and AI analytics with maintenance workflows for industrial operators. Treon Flow targets material handling conveyors and other plant equipment, while Treon Make covers manufacturing. Both sit within its cloud based prescriptive maintenance offering.

The company announced a €6.8 million investment in April 2026, led by ACME Capital as part of a Series A extension. Treon describes its solutions as managed subscription services, combining sensors, analytics, mobile interfaces and automated workflows. The same announcement says it serves manufacturing, material handling and logistics customers. Its service includes wireless sensors and automated workflows.

Uptake sells predictive maintenance for commercial fleets, including buses, heavy trucks, construction vehicles and mining equipment. Its software combines fleet data, remote diagnostics and risk analysis to identify problems and prioritize maintenance. Dashboards let teams filter alerts by time, severity, location and affected system.

Bosch announced plans to acquire Uptake in March 2026. Uptake also provides a mixed fleet data hub that standardizes information across assets, plus sensor analysis and tools for identifying eligible warranty claims. The company offers a sales demo for operators considering the service, and lists separate Fleet, Defense and Tires products.

UptimeAI sells industrial root cause and maintenance agents for oil and gas, chemicals, cement, power generation and process operations. Its software connects time series data, work orders and equipment manuals to investigate problems across related assets. Maintenance planners receive recommendations for tasks and service frequencies based on failure patterns and operating conditions.

ABB took a minority stake in UptimeAI in March 2025, with financial terms undisclosed. The partnership initially targeted cement, metals and water pipe industries in India. UptimeAI’s maintenance workflow lets planners review and approve changes before pushing them to the CMMS.

ABB’s Genix Asset Performance Management combines sensor data and AI to diagnose developing equipment failures for industrial operations such as oil and gas, chemicals, power and water. It uses analytical AI and physics based models for equipment predictions. The Genix APM Copilot adds a conversational interface for investigating findings and retrieving contextual information.

ABB introduced the expanded Copilot capabilities in September 2025. They include event categorization, maintenance prioritization and workflows for generating work orders and triggering corrective actions. Copilot supports cloud and local deployment, including hybrid and edge environments, and can work with multiple language models.

Schneider Electric wholly owns AVEVA, which sells Predictive Analytics for industrial equipment maintenance. The software detects equipment anomalies, diagnoses faults and estimates time to failure. Its no code environment lets users deploy, validate and interpret predictive models without programming or data science knowledge.

The product includes predefined equipment templates to support deployment and diagnostic tools that identify which sensors contribute to a fault indication. Its Asset Library supplies recommended remediation actions. Time to failure forecasts help teams decide between an urgent shutdown and maintenance during a scheduled outage. AVEVA also supports single sign on and administrator controls for user access and editing rights.

Baker Hughes’ Cordant combines machinery sensing and analytics for industrial asset reliability. Its software uses AI and machine learning to detect degradation, prioritize corrective work and connect equipment health with maintenance strategy. The offering includes technology from Bently Nevada and ARMS Reliability.

Cordant’s Asset Health service combines condition monitoring, cloud applications and diagnostic expertise. Automated analysis uses machine learning and physics based models to detect anomalies and recommend action, with specialists reviewing findings and adding context. The broader suite also provides asset strategy and defect elimination applications, allowing teams to expand from equipment monitoring into maintenance planning and investigation of recurring problems.

C3 AI Reliability predicts equipment failures for asset operators by combining sensor readings, maintenance records and parts inventory. It uses time series and machine learning models for early warnings, then generates failure mode diagnoses and recommendations to support maintenance scheduling. The application covers discrete, continuous, batch and semi batch production processes.

The software unifies multiple data sources, including documents and process diagrams, in a shared model. Operators can search asset history and institutional knowledge through an embedded search and chat interface. Root cause diagnostics help prioritize maintenance cases, while the same application provides monitoring across assets and sites.

GE Vernova’s APM includes SmartSignal predictive analytics for industrial equipment operators. SmartSignal compares live sensor readings with learned normal behavior for parameters such as load, temperature, pressure, vibration and ambient conditions. It turns deviations into prioritized diagnoses and forecasts when equipment will reach an alarm limit.

SmartSignal is available within the broader APM suite or with software purchase and subscription service options. GE Vernova’s monitoring centers can provide continuous monitoring, issue triage, remediation guidance and analytics management, including a fully outsourced offering. The company presents EDP as a customer example and offers custom private pricing through AWS Marketplace.

Honeywell Technologies supplies the Forge software portfolio. Its industrial Asset Performance Management offering uses AI and machine learning to analyze equipment and process data, identify potential failures, estimate time to failure and recommend corrective actions. It serves reliability, maintenance, process and controls engineers monitoring rotating, electrical, static and mobile assets.

The software runs locally or as SaaS, with cloud options that include Honeywell Forge. Its analytics combine learned models with engineering models and embedded domain knowledge. Honeywell also offers consulting and value delivery services, and integrated maintenance workflows connect reliability findings with the teams and systems responsible for repairs.

IBM’s Maximo Application Suite combines asset management and AI for businesses maintaining physical equipment. Its suite includes monitoring, equipment health and predictive maintenance applications. IBM offers SaaS and client managed packages, with licensing based on credits called AppPoints.

The SaaS plan structure includes Essentials, Standard and Premium. Standard combines enterprise asset management, asset performance management and reliability centered maintenance, while Premium adds options for specific maintenance, disaster recovery and data retention needs. IBM’s package comparison names Monitor, Predict, Assist and Schedule Optimizer among the suite applications. Buyers can request a price estimate based on their intended deployment and usage.

SAP’s Asset Performance Management analyzes IoT and maintenance data for asset owners, plant managers and reliability engineers. Its AI features estimate equipment failure probabilities and remaining useful life. The cloud application runs on SAP Business Technology Platform and supports remote equipment monitoring and maintenance strategy planning.

Beyond prediction, SAP APM supports reliability centered maintenance and failure mode effects analysis to develop asset strategies and recommendations. Risk estimation helps teams assess consequences and select maintenance methods. SAP names Aker BP as a customer using APM for predictive maintenance and offers a product tour and sales demo.