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Solution

Predictive Maintenance

Trend condition and prognostic indicators to plan inspections, spares and intervention before functional failure.

Published technical information Tachyonic Intelligence Sdn Bhd
AI-assisted industrial machine diagnostics and monitoring solutions
CategorySolution
Customised developmentEnd-to-end engineering for application-specific products, performance optimisation, compliance readiness and scalable future upgrades.
Integrated I/ODigital, analogue and special-purpose I/O with Ethernet, fieldbus, GSM, GPS and high-speed interface options.
Industry focusWater treatment, transportation, palm oil refining, fuel depots, substations, manufacturing, facilities monitoring and vehicle systems.

Move from detection to planning

Predictive maintenance requires a reliable baseline, stable data quality and a process for converting condition evidence into work. Remaining Useful Life, failure probability, degradation index and time-to-maintenance can support planning when they are reviewed together with vibration, temperature, speed and maintenance history.

Use prognostic values as estimates, validate them against the actual machine and revise the baseline after major maintenance or operating change.

Rotating-machine fault recognition

Connect vibration signatures to the motor components that need attention

This exploded motor view shows where common mechanical, bearing and rotor-related conditions develop and how vibration monitoring supports a structured maintenance investigation.

Exploded electric motor showing bearing, rotor, shaft, fan, alignment and mounting faults that PyXis vibration monitoring can help identify Enlarge diagram
Exploded motor view linking diagnostic signatures to practical inspection locations.
01
Faults mapped to motor components

The guide highlights outer-race and inner-race bearing faults, rolling-element defects, misalignment, rotor eccentricity, rotor-bar or electrical abnormalities, shaft imbalance, bent shaft, cooling-fan faults and mechanical or mounting looseness.

02
How PyXis supports diagnosis

PyXis combines triaxial vibration, waveform and FFT information, bearing-frequency patterns, harmonics and condition trends to support earlier recognition and maintenance prioritisation. Findings should be checked against machine speed, load, bearing geometry, mounting quality and operating history.

BearingsRotorShaftAlignmentFanMounting

Diagnostic guide: detectability and confidence vary with sensor location, installation quality, machine construction, operating state, baseline quality, analysis settings and firmware profile.

Technology insights

Professional engineering articles

Practical guidance derived from the current PyXis and Titania technical material.

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