Model T Pipeline — MT-007 — Predictive Maintenance

POWERSENSE

Predictive Maintenance Through Electrical Intelligence — Reduce Downtime 40-60% Through AI-Powered Electrical Monitoring

DATE: 2026-02-14 PROJECT: MT-007 VERTICAL: Industrial Motors STATUS: Concept ARCHITECTURE: Edge-First ML

Executive Summary

Problem: Industrial equipment fails unpredictably, costing facilities €125K-260K/year in unplanned downtime.R8

Solution: PowerSense monitors electrical signatures through existing Tele Haase relay infrastructure to predict motor failures days to months in advance depending on failure mode — from 1-4 days for bearing anomalies via MCSA (64-94% accuracy)R11 to 6-18 months for stator winding degradation — enabling planned maintenance instead of emergency repairs.

Partnership: Joint product combining Tele Haase's market access & electrical relay hardware with Promwad's IoT platform, ML models, and cloud expertise.

ROI: 8-12 month payback through €144K-234K annual savings per typical 200-motor facility (40-60% downtime reduction).R2

€125K-260K
Annual Downtime Cost
40-60%
Downtime Reduction
1 Day — 18 Mo
Prediction Window (by failure mode)
8-12 Months
ROI Payback Period

The Problem

The Industrial Downtime Crisis

Industrial facilities face a critical reliability problem: unplanned motor failures cost €125K-260K per year in lost production, emergency repairs, and customer delays.R8

Scale of the Problem:

  • $50 billion annually in unplanned downtime costs across industries (Deloitte)R9
  • €260,000 per hour average downtime cost for manufacturing (Aberdeen Group, Gartner)R8
  • 6.3% average downtime from motor failures alone in European industries
  • 40-50% of industrial energy consumption is electric motorsR10

Current Maintenance Approaches Are Inadequate

1. Reactive Maintenance (Run-to-Failure):

  • Emergency repairs cost 3-5× more than planned maintenance
  • Unexpected downtime disrupts entire production schedules
  • Safety risks from catastrophic failures
  • Lost production revenue

2. Preventive Maintenance (Time-Based):

  • 30% of preventive maintenance is unnecessary (over-maintenance)
  • Replacing parts with remaining useful life wastes capital
  • Doesn't prevent unexpected failures between service intervals
  • High labor costs for scheduled inspections

3. Vibration-Based Predictive Maintenance:

  • Requires dedicated sensors on every machine (SKF IMx, Emerson AMS)R4
  • Installation requires production shutdown
  • High upfront CapEx (€500-€2,000 per machine)
  • Not economical for "balance of plant" equipment (80% of assets)

What's Missing:

A non-intrusive, low-cost solution that can monitor the entire facility's rotating equipment without installing sensors on each machine.

PowerSense Solution: Leverage electrical parameters measured at the panel level (where Tele Haase relays already exist) to detect mechanical failures before they occur.

The Solution

The Insight: Electrical Signatures Predict Mechanical Failures

The Physics Connection

Mechanical degradation creates measurable electrical signatures weeks before failure. PowerSense leverages Motor Current Signature Analysis (MCSA) and Electrical Signature Analysis (ESA) to detect these patterns non-intrusively.

Fundamental Physics:

  • Bearing Wear → Friction Increase → Current Spike (+5-15%)
  • Rotor Imbalance → Load Oscillation → Power Fluctuations
  • Insulation Degradation → Leakage Current → Ground Faults
  • Overheating → Resistance Change → Voltage Drop
  • Broken Rotor Bars → Harmonic Distortion → Frequency Sidebands

Predictive Windows for Common Failure Modes

Failure Mode Electrical Signature Predictive Window Detection Accuracy Source
Bearing Anomaly (MCSA) Current harmonics (sidebands), THD increase 1-4 days 64-94% CRAR11 Bermeo-Ayerbe et al., 2023
Bearing Wear (Vibration P-F) Progressive degradation trend via vibration correlation Up to 9 months Stage-dependentR4 Schaeffler / Dr. S.J. Lacey
Broken Rotor Bars Sidebands at (1±2s)f, harmonic distortion Up to 6 months Vendor-validatedR1 Artesis e-MCM (10M+ profiles)
Stator Winding Degradation Partial discharge, leakage current increase 6-18 months ~10% error IEEE papers (multiple)
Rotor Imbalance Power oscillations, current modulation @ 1x RPM Weeks to months 80-90% Industry consensus
Overload/Overheating Current increase, voltage drop, PF decrease Real-time to weeks 95%+ Relay threshold-based

Key Insight: Different failure modes create distinct electrical "fingerprints" — enabling automated classification without vibration sensors.

How PowerSense Works

End-to-End Data Flow Example

Scenario: Motor #47 (Water Pump, 15 kW) develops bearing wear.

Motor #47 (Water Pump) ↓ Measures I/V/P @ 10 kHz Tele Haase IoT Relay ↓ FFT analysis, feature extraction • RMS current: 18.3 A (baseline: 17.5 A → +4.6% increase) • THD: 0.08 (baseline: 0.05 → +60% increase) • Bearing fault frequency (BPFO @ 327 Hz): 0.12 (baseline: 0.02 → +500%) ↓ Edge ML (Isolation Forest) • Anomaly score: 0.85 (threshold: 0.8) • Decision: ANOMALY DETECTED ↓ Cloud LSTM Model • RUL Prediction: 18 days ± 5 days (confidence: 82%) ↓ Random Forest Classification • Failure type: Bearing wear (89% confidence) ↓ Alert Engine • Severity: YELLOW (RUL >14 days) • Recommended action: "Schedule bearing replacement during next maintenance window" ↓ Webhook to SAP PM Auto-create Work Order • Notification ID: N-12345 • Parts needed: Bearing 6308-2RS (SKF), Grease NLGI 2 ↓ Dashboard + Mobile App • WebSocket push notification • Email to facility manager • SMS to maintenance supervisor ↓ Technician schedules maintenance → Failure prevented!

Architecture

4-Layer Technical Stack

Layer 4: Applications (Web Dashboard, Mobile App, Desktop Client) ↕ REST API / GraphQL / WebSocket Layer 3: Cloud Platform (AWS IoT Core / Azure IoT Hub) - ML Pipeline: Isolation Forest + LSTM RUL + Random Forest Classification - Data Storage: InfluxDB (time-series) + PostgreSQL (metadata) - Integrations: SAP PM, IBM Maximo, OPC UA ↕ MQTT / HTTPS Layer 2: Gateway (Optional, NXP i.MX 8M aggregation) - Local ML inference, Modbus master, Qt/QML HMI - Handles 50-500 motors per site ↕ Modbus TCP/RTU / MQTT Layer 1: Edge Intelligence (IoT-Enabled Tele Haase Relay) - High-precision ADC (TI ADS1256, 24-bit, 30 kSPS) - MCU: STM32F7 / NXP i.MX RT (Cortex-M7, 216 MHz) - Edge ML: TensorFlow Lite (Isolation Forest anomaly detection) - Data compression: 1,200× reduction (1 KB/min vs 1.2 MB/min raw) ↕ Electrical Measurement (I, V, P @ 10-20 kHz) Motor / Panel - Current, Voltage, Power Signals

Layer 1: Edge Intelligence (Tele Haase IoT Relay)

Hardware Specifications:

  • ADC: TI ADS1256 (24-bit, 30 kSPS, 8 channels)
  • MCU: STM32F765 or NXP i.MX RT1050 (Cortex-M7, 216 MHz, DSP)
  • Cellular: Quectel BG95-M3 (4G Cat-M1/NB-IoT) OR Modbus RTU/TCP
  • Current Sensing: Split-core CT (non-invasive, 100A max)
  • Voltage Sensing: Fused taps + resistive divider (100-690 VAC)
  • Power: 24V DC (<5W consumption)

Real-Time Signal Processing: 10-20 kHz sampling → FFT (2048 points) → Feature extraction (40-60 features: RMS, harmonics, THD, kurtosis) → Edge ML inference (Isolation Forest anomaly score) → Data compression → MQTT publish to cloud.

Layer 3: Cloud ML Pipeline

ML Model Purpose Algorithm Performance
Anomaly Detection Detect abnormal operation Isolation Forest Precision: 75-85%, Recall: 85-95%
RUL Prediction Days until failure LSTM Neural Network ±5-10 days for 30-day predictions
Failure Classification Identify failure type Random Forest Accuracy: 85-92%

Layer 4: Applications

  • Web Dashboard (React + TypeScript): Fleet overview, motor details, alert history, analytics, reports
  • Mobile App (React Native, iOS/Android): Push notifications, work order management, QR code scanner
  • Desktop Client (Qt/QML, optional): On-premise dashboard, deep diagnostics, waveform viewer

Business Value & ROI

Value Proposition

For Industrial Customers

40-60%
Downtime Reduction
€144K-234K
Annual Savings (200-motor facility)
8-12 Months
Payback Period
100%
Equipment Coverage

Additional Benefits:

  • Non-intrusive installation: Electrical panel level, no motor shutdown required
  • 100% coverage economically viable: €150-600/motor (vs. €500-2,000 vibration)
  • 3-5× emergency repair cost savings: Planned maintenance vs. reactive fixes
  • Extended asset life: 15-20% through optimized maintenance

For Tele Haase

Transform from €50 relay component supplier → €10-50/month SaaS platform provider with 10-20× customer lifetime value increase.

Metric Before PowerSense After PowerSense
Business Model One-time hardware sale Hardware + recurring SaaS
Customer LTV €50-150 (relay) €410-1,850 (3-year)
Gross Margin 30-40% (hardware) 60-70% (blended, SaaS-heavy)
Year 3 Revenue €5-8M (hardware only) €33M (hardware + SaaS + services)

For Promwad

  • Market Access: Tele Haase's 100,000+ installed relays across Europe (warm leads)R16
  • Recurring Revenue: €10-13M/year by Year 3 (50% SaaS revenue share)
  • Technology Showcase: End-to-end IoT + ML capability (hardware + embedded + cloud + AI)
  • Platform Reusability: Same architecture for HVAC, pumps, compressors, generators
€16M-24M
Promwad Annual Revenue (Year 5)
10-20×
ROI Multiple (Year 5)

Investment & ROI Analysis

Total Investment (24 months)

Combined Investment: €1.7M-2.5M (Tele + Promwad)

Revenue Projections (3-Year)

Year Customers Motors Total Revenue Revenue Mix
1 5-20 500-2K €1M-3M Hardware: €0.25M, SaaS: €0.5M, Services: €0.1M
2 20-50 2K-5K €3.5M-8.5M Hardware: €1.25M, SaaS: €3M, Services: €0.5M
3 50-100 5K-10K €9M-22M Hardware: €2.5M, SaaS: €6M, Services: €1M

Promwad ROI (50% SaaS Share)

€2.5M-11M
3-Year Total Revenue
1.7× - 7.3×
ROI Multiple
12-24 Months
Payback Period

Tele Haase ROI

€10M-30M
3-Year Total Revenue
10× - 30×
ROI Multiple
6-12 Months
Payback Period

Competitive Positioning

Competitive Comparison

Feature PowerSense Artesis e-MCM ABB Samotics SAM4 SKF IMx (Vibration) Samsara (Generic IoT) Fluke (Power Analyzer)
Technology Electrical signatures MCSA-based, cloud platformR1 ESA-based, cloud analyticsR9 Vibration sensors Generic telemetry Power analyzer
Installation Panel-level, non-intrusive Panel-level, non-intrusive Panel-level, non-intrusive On-motor (invasive) Custom sensors Manual testing
Coverage 100% of motors 10M+ motor profiles ABB ecosystem motors 5-10% critical only Depends on investment Ad-hoc
Cost/Motor €150-600 €500-1,500/motor setup Higher enterprise pricing €450-1,100 €300-800 €5K-10K
Prediction Window 1-4 days (bearing/MCSA)
up to 6-18 months (rotor/stator)		 Up to 6 months (vendor-claimed) Weeks to months (ESA) 7-21 days N/A N/A
ML Capability RUL + classification Patented ML, cloud-only (no edge) Cloud analytics, ABB infrastructure required Basic anomaly Rule-based None

UNIQUE POSITION

"The only predictive maintenance solution that leverages existing electrical infrastructure to monitor 100% of motors at 60% lower cost, delivering advance failure notice from days (bearing/MCSA) to months (rotor/stator degradation) through AI-powered electrical signature analysis."

Business Model

White-Label Structure (Recommended)

Product branding: "TELE PowerSense" or "PowerSense by TELE HAASE"

Revenue Stream Tele Haase Promwad Rationale
Hardware Sales (IoT Relays) 100% 0% Tele manufactures and sells
SaaS Subscriptions 50% 50% Equal value: customer access + platform
Installation Services 100% 0% Tele field teams perform on-site work
Custom ML Models 30% 70% Promwad provides advanced engineering

Investment Commitment (24 months)

Promwad Investment: €900K-1.3M

  • Platform development (€500K-700K): Cloud infrastructure, ML pipeline, APIs
  • ML model development (€200K-300K): Anomaly detection, RUL prediction, classification
  • Edge firmware (€100K-150K): FFT, feature extraction, data compression
  • Testing & validation (€100K-150K): Pilot support, accuracy validation

Tele Haase Investment: €700K-1M

  • Hardware R&D (€300K-400K): IoT-enabled relay prototypes, certifications
  • Pilot program (€200K-300K): Hardware at cost, installation labor
  • Sales & marketing (€200K-300K): Training, materials, trade shows

Total Combined Investment: €1.6M-2.3M

Implementation Roadmap

Phase 1: Pilot Program (Months 1-6)

Goal: Prove technical feasibility and customer ROI with real deployments

  • 3-5 pilot customers (Siemens, ABB, renewable energy sites)
  • 50-200 IoT relays deployed
  • ML model training on real failure data
  • Success metrics: 80%+ prediction accuracy, 30%+ downtime reduction
  • Investment: €300K-500K

Phase 2: Scale (Months 7-18)

Milestone Timeline Target
Product Certifications Months 7-9 CE, UL, IEC 61010 compliance
Commercial Launch Month 10 First 20 paying customers
Channel Partnerships Months 12-18 Distributors, system integrators
Revenue Target End Month 18 €1M-3M (Year 1)

Phase 3: Market Leadership (Months 19-36)

100+
Customers
10,000+
Motors Monitored
€9M-22M
Year 3 Revenue

Vertical Expansion: Manufacturing → Renewable energy → Building automation

Geographic Expansion: DACH → EU → Global

Ecosystem: CMMS integrations (SAP PM, IBM Maximo), partner network, API marketplace

Promwad Competencies

Promwad brings 20+ years of embedded systems and industrial IoT expertise to PowerSense:

Relevant Experience

  • Industrial IoT: Vibration monitoring, edge ML, predictive maintenance platforms
  • Electrical Engineering: Current/voltage sensing, electrical signature analysis, power electronics
  • Automotive Electronics: 300K+ telematics units (4G LTE + CANBUS), proven at scaleR18
  • Edge ML: TensorFlow Lite Micro, LSTM models, real-time anomaly detection
  • Industrial Protocols: Modbus, OPC UA, MQTT, RS-485 integration

Delivery Capabilities

  • Hardware Design: Industrial-grade sensors, DIN-rail enclosures, IP65 rated
  • Firmware: Real-time DSP, FFT/STFT processing, edge inference
  • Cloud Platform: Multi-tenant SaaS, PostgreSQL+TimescaleDB, MQTT/AWS IoT Core
  • ML Pipeline: Current Signature Analysis (CSA), LSTM RUL prediction, Random Forest classification
  • Certifications: ISO 9001, ASPICE Level 2, IEC 62304
  • Partnerships: NXP, Qualcomm, Qt, Nordic Semiconductor

Why Promwad for PowerSense:

PowerSense requires deep electrical engineering expertise (current signature analysis), industrial protocol integration (Modbus/OPC UA), and multi-tenant cloud architecture. Promwad has proven this stack in automotive telematics (300K+ units) and industrial IoT platforms.

References & External Links

#SourceCategory
R1Artesis — Motor Current Signature Analysis (MCSA), GE Energy affiliateCompetitive Intelligence
R2Artesis ROI CalculatorCompetitive Intelligence
R3MotorDoc — EMPOWER Analyzer, EmpathCMSCompetitive Intelligence
R4SKF — Condition monitoring systemsCompetitive Intelligence
R5Sensemore — Predictive maintenance platformCompetitive Intelligence
R6Schneider Electric — Motor management and asset advisor solutionsCompetitive Intelligence
R7MarketsandMarkets — Predictive maintenance market analysisMarket Research
R8Siemens: True Cost of Downtime 2024Market Research
R9ABB Industrial Downtime Study 2025 — Global survey of 3,600 decision-makersMarket Research
R10EU-MORE Motor Policy Review (D2.2, D2.3, 2024-2025) — 300M electric motors in EU industryMarket Research
R11Bermeo-Ayerbe, M.A. et al. (2023). Remaining useful life estimation of ball-bearings based on motor current signature analysis. Universitat Politècnica de Catalunya / Université de Lille. Validated on PRONOSTIA-FEMTO dataset.Academic
R12TensorFlow Lite for MicrocontrollersTechnical Documentation
R13AWS IoT CoreTechnical Documentation
R14PostgreSQLTechnical Documentation
R15InfluxDB — Time-series databaseTechnical Documentation
R16TELE Haase Official WebsitePartner Information
R17Promwad Company ProfilePartner Information
R18Promwad: Automotive IoT SolutionsPartner Information
R19Promwad: Industrial ElectronicsPartner Information
R20Schaeffler Bearing Analysis Resources — Bearing damage detectionTechnical Documentation
R21IEC 60034 — Rotating electrical machines standardsStandards
R22NEMA MG-1 — Motor and generators standardsStandards
R23Deloitte Predictive Maintenance Study — PdM ROI benchmarksMarket Research
R24Aberdeen Group — Downtime cost analysisMarket Research
R25Copper Ridge Mining Case Study — 42% downtime reduction, $3.2M savingsCase Study

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