ML / AI Algorithm Development for Predictive Maintenance
Generate clean, labelled fault datasets, capture high-channel-count parallel sensor streams, and validate classifier outputs against ground-truth spectral features — without leaving your lab.
Machinery Fault Signature Simulator
₹13,53,600
Indicative price, ex-works. Excludes GST, packing & forwarding, freight, insurance and duties. Request a quotation to confirm.
Tiera’s Machine Fault Simulator (TMFSS) is a valuable tool for industries and researchers, simulating over 30 real-world faults such as: Bearing faults: outer race defects, inner race defects, cage defects. Motor faults: stator faults, rotor faults, electrical unbalance. Gearbox faults: gear wear, misalignment, gear tooth damage. And more.
Tiera’s Machine Fault Simulator (TMFSS) is a valuable tool for industries and researchers, simulating over 30 real-world faults such as: Bearing faults: outer race defects, inner race defects, cage… Read more
defects. Motor faults: stator faults, rotor faults, electrical unbalance. Gearbox faults: gear wear, misalignment, gear tooth damage. And more.Description
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Tiera’s Machine Fault Simulator (TMFSS) is an invaluable tool for industries and researchers, providing a comprehensive platform to study and analyze over 30 real-world faults commonly observed in industrial settings. The TMFSS is designed to enhance understanding and improve diagnostic skills through practical simulation. It covers a wide range of faults, including: Bearing Faults: Outer race defects, inner race defects, and cage defects. Motor Faults: Stator faults, rotor faults, and electrical unbalance. Gearbox Faults: Gear wear, misalignment, and gear tooth damage. Pump Faults: Cavitation, flow turbulence, and impeller wear. Fan Faults: Blade damage and imbalance. Coupling Faults: Misalignment and looseness. Belt Drive Faults: Belt wear and misalignment. The TMFSS macro allows users to simulate these faults and gain hands-on experience in fault detection and analysis.
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Base unitMachinery Fault Signature Simulator
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ML / AI Algorithm Development for Predictive Maintenance Generate clean, labelled fault datasets, capture high-channel-count parallel sensor streams, and validate classifier outputs against ground-truth spectral features — without leaving your lab. Vibration Analyst Certification Training ISO 18436-2 Cat I and Cat II training programmes combining hardware fault rigs, structured course material, and digital simulation software — for training institutes and in-plant L&D teams. Wireless Sensor Validation Lab Characterise a wireless accelerometer against a wired reference, induce known faults on a controlled rig, and capture parallel streams for ground-truth labelling — all without a trip to a customer site.
Vibration Analyst Certification Training
ISO 18436-2 Cat I and Cat II training programs combining hardware fault rigs, structured course material, and digital simulation software — for training institutes and in-plant L&D teams.
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₹13,53,600
Indicative price, ex-works. Excludes GST, packing & forwarding, freight, insurance and duties. Request a quotation to confirm.
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The engineering behind this product
Why this measurement is made, how it goes wrong, and what the numbers mean — written by the people who build the instrument.
Interactive explainerBuild the Spectrum Yourself: What Unbalance, Misalignment and Looseness Actually Look LikeAn interactive mini-explainer: assemble a machine's vibration from its parts — 1x, 2x, a harmonic comb — and watch the time waveform and spectrum change live. Learn the three classic signatures by driving them.9 min read · Read →
Fault simulationBuilding a Controlled Machinery Fault Library with TMFSSA visual mini-explainer: how a lab reproduces unbalance, misalignment, looseness, bearing defects and gear faults on demand, and why a label you set before capture beats a label you guess afterwards.7 min read · Read →
AI datasetsML / AI Algorithm Development for Predictive MaintenanceHow controlled machinery faults, synchronized vibration capture, and analyst-labelled features create useful training data for predictive-maintenance models.8 min read · Read →