Solution

Predictive Maintenance and Condition Monitoring

We build condition monitoring that watches vibration, temperature and load continuously, so maintenance acts on the state of the equipment rather than the calendar.

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Our clients

Siemens
Anadolu Isuzu
BSH
Tofaş
Toyotetsu
Martur
Turkcell
Vodafone
Doğadan
Pakmaya
Bross
Abdi İbrahim
Nobel İlaç
Plastimak
Dalgıç Kalıp
Barbaros Motor
Magneti Marelli
Rözmaş
Sanel

Predictive maintenance means servicing equipment based on its measured condition, before it fails but not earlier than needed. Preventive maintenance follows the calendar: it replaces healthy bearings and still misses the failure that arrives between intervals. Predictive maintenance follows trends in vibration, temperature, motor current and run hours.

A good program starts with asset selection, not sensors. Equipment with the highest downtime cost, no redundancy and a measurable failure mode comes first. Many useful signals already sit in the PLC, such as motor current, drive temperature and cycle counts, and vibration or temperature sensors fill the gaps.

VOLSOFT collects that data on Proxus, establishes a baseline per asset, writes trend and anomaly rules and routes alerts into your maintenance workflow. Machine learning is one tool in that chain; clean data and understandable rules come first.

What we deliver

Predictive Maintenance

Criticality analysis

Assets are ranked by downtime cost, redundancy and failure history, and monitoring starts with the most critical ones.
Vibration and temperature

Velocity RMS, acceleration and bearing temperature are tracked on motors, pumps, fans, gearboxes and compressors, interpreted with zone-based limits such as those in ISO 20816.
Signals already in the PLC

Motor current, drive temperature, run hours, cycle and start counts are read from the PLC and used as condition indicators without extra sensors.
Trend and anomaly alerts

Beyond fixed thresholds, we track deviation from each asset's own baseline and the rate of change, so the alert comes before the breakdown.
Maintenance workflow

Alerts can open work orders in your CMMS, and MTBF and MTTR reporting is fed by real failure records.

How a project runs

From discovery to live, step by step

01
Assets and failure modes

With your maintenance team we select critical equipment and the failure modes to watch: bearing wear, misalignment, imbalance, overheating.
02
Measurement plan

We list what can be read from the PLC and which sensors must be added, including mounting points. Hardware comes from standard products.
03
Baseline

Data is collected and each asset's normal behavior is characterized across load and speed conditions.
04
Rules and workflow

Trend and anomaly rules go live, alerts are routed to maintenance and work orders, and results are reviewed regularly.

Built on Proxus

Condition data is collected and stored in the Proxus historian; baseline, trend and anomaly rules run on the platform, and alerts go out by email, message or CMMS integration.

Proxus is the industrial data platform built by the VOLSOFT team.

Condition monitoring with Proxus ↗

Frequently asked questions

Common questions

What is predictive maintenance?

Predictive maintenance schedules work based on the measured condition of equipment, such as vibration, temperature and current, instead of fixed intervals. The goal is to catch developing faults before they cause downtime while avoiding unnecessary part replacement.

Predictive vs preventive maintenance: what is the difference?

Preventive maintenance follows a calendar or run-hour interval; predictive maintenance follows measured condition. Preventive is simpler to plan but can replace healthy parts and miss failures between intervals. Most plants use both, shifting critical assets toward predictive.

What data do you need for condition monitoring?

For basic monitoring, vibration velocity RMS, acceleration and temperature are enough; detailed bearing and gear diagnosis needs frequency spectra. Speed and load context, usually read from the PLC, is essential for interpreting the values.

Do we need AI for predictive maintenance?

Not to start. Baselines, trend rules and well-chosen limits catch many developing faults. Anomaly detection models add value once there is clean, contextualized history, and Proxus supports them as one capability among others.

What should a predictive maintenance solution provider deliver?

A working loop from measurement to maintenance action: asset selection, measurement plan, data collection, baselines, rules and integration into your work order process. VOLSOFT delivers that loop on Proxus and does not resell sensors.