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INTRODUCTION

Machine Down Time Monitoring 

A Machine Downtime Reporting System is a tool or module within industrial monitoring and maintenance systems that tracks, records, analyzes, and reports periods when machinery is not operational—either due to breakdowns, maintenance, or other issues

🎯 Purpose:

To reduce production losses and improve operational efficiency by:

  • Identifying causes of downtime

  • Monitoring frequency and duration of downtime events

  • Supporting preventive maintenance planning

  • Improving machine utilization

🛠️ Key Features of a Machine Downtime Reporting System:

  1. Automatic and Manual Logging:

    • Sensors/PLC detect when a machine stops

    • Operators can manually enter downtime reasons (e.g., maintenance, tool change, material shortage)

  2. Downtime Categorization:

    • Scheduled vs. unscheduled

    • By cause: mechanical failure, electrical issue, operator error, etc.

  3. Real-Time Alerts:

    • Notify maintenance teams or supervisors when machines stop unexpectedly

  4. Reports and Dashboards:

    • Daily/weekly/monthly downtime reports

    • Top 10 machines by downtime

    • Pareto analysis of downtime causes

  5. Integration with Other Systems:

    • Works with MES (Manufacturing Execution System), SCADA, ERP, or CMMS (Maintenance systems)

System Architecture

🎯 1. Data Acquisition Layer

  • Edge or PLC Devices / Sensors: Collect machine telemetry (e.g. status, run/stop signals). 

  • Connectivity Middleware: For on-prem machines, solutions like AWS IoT Greengrass or MQTT brokers capture and securely forward data. 

🛠️ 2. Ingestion & Real-time Processing

  • Streaming Pipeline: Use services such as Amazon Kinesis Data Streams/Firehose, or open-source counterparts (Kafka) to ingest live telemetry. 

  • Filtering & Aggregation: Serverless functions (e.g. AWS Lambda) detect downtime events, calculate durations, and aggregate metrics.

 

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🔍 3. Storage, Enrichment & Metadata

  • Real-time DB: Systems like DynamoDB store current machine status, tagging/grouping configurations for fast dashboard updates.

  • Historical Archive: Telemetry gets persisted to data stores like S3 for long-term insights. ◆

  • Catalog + BI Prep: AWS Glue catalogs S3 data, with query/reporting via Athena and visualization in Quick Sight. 

📊 4. Visualization & Reporting

  • Real‑time Dashboard: Web app (hosted on S3 + CloudFront with AWS AppSync or equivalent). Displays heatmaps, status tiles, downtime trends. (shown in image 1).

  • Historical Reports: BI dashboards spotlight trends (e.g. pie charts of downtime reasons, time-series charts; see image 2).

  • Operator Input UI: Tablets or kiosks at machines prompt operators to classify stops (planned vs unplanned, reason, operator). 

⚠️ 5. Alerts & Decision Support

  • Anomaly / Predictive ML: ML models or rule-based systems flag abnormal downtime or forecast failures.

  • Event Correlation: Tools like Prometheus/Alert manager/Grafana can correlate downtime with other system metrics. 

  • Notifications: Alerts are routed via email, SMS, or Slack when thresholds (SLA breaches, repeated faults) occur.

👉 6. Integration & Feedback

  • Plant Integration: Tie outputs into MES/ERP systems to feed maintenance schedules, production planning.

  • Continuous Improvement: Historical root-cause analysis and KPI monitoring enable process refinement

Overview Dashboard

*Utilization Rate: ​

Utilization Rate%: We are calculating the utilization of machine in percentage. Formula = (Operational Run Time/Total Run Time)/100

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*Average Cycle Rate 

Avg Cycle Time: It is the Average Cycle Time of the Molding Machine Cycle. Formula: Operational Time / Cycle Counts

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*Operational Time

Operational Time: If the machine feeding is not done within 2 minutes and machine hydraulic motor is on then the system stops the counting of operational time

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*Total Time

Total Time: Total runtime of machine is calculated by the run status of the hydraulic motor.

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*Cycle Count

Cycle Counts: Counting of the cycles is being done by the status of the machine door close feedback.

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*Machine Status

Machine Status: Realtime Machine Status (If hydraulic Motor is on then it shows ON)

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*Line Chart

Line Chart: Red line shows the Machine run time per hour and green line shows the operational time per hour

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*Colum Chart

Cycle Counts Per Hour

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*Navigation Menu

*Real Time Closed And Heating Status

*Real Time Machine Status 

Reports

1. Navigation Menu

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2. Select Timespan For Report

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3. Select Interval

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4. Click Download Report To Get Report

Daily Report

A daily machine downtime report is a structured summary—typically prepared at the end of each shift or day—designed to capture downtime events accurately, analyze issues, and drive continuous improvement. Here's how to build one effectively:

1. Objectives of a Daily Report

  • Log every downtime event (planned and unplanned).

  • Record key details: when, which machine, for how long, and why.

  • Assign responsibility and capture verification.

  • Provide immediate insights and prompt corrective actions.

📋 2. Essential Sections & Fields

A typical Daily Downtime Report Form includes:

  • Report Metadata: Date, Shift, Prepared by, Location.

  • Event Entries (for each downtime):

    • Machine ID

    • Downtime Start & End

    • Duration (auto-calculated or manually entered)

    • Category (e.g., mechanical issue, setup change, material shortage, other)

    • Description/Reason (free-text, dropdown, or checklist)

    • Planned vs. Unplanned (important for OEE and reliability analysis)

  • Verification: Verified by (person), signature or approval stamp.

  • Operator/Maintenance Notes: Optional comments or steps taken.

Hourly performance report

Downtime Reporting Supervisory Dashboard

A Downtime Reporting Supervisory Dashboard is a visual platform designed for supervisors and plant managers to monitor, analyze, and respond to equipment downtime in real-time and over historical periods. Here's a comprehensive breakdown:

Purpose & Benefits

  • Real-time detection: Shows which machines are running, idle, or in downtime right now.

  • Root cause insight: Displays Pareto charts and breakdowns by reason to highlight the key issues quickly 

  • Performance comparison: Provides side‑by‑side views of shifts, machines, or lines to benchmark performance and identify weak spots

Downtime Reporting Display Module

A Downtime Reporting Display Module is a dedicated interface designed to clearly and visually present machine downtime data—making it easy for supervisors, operators, and managers to monitor, understand, and act on equipment stoppages.

🧩 What It Does

  • Show key downtime KPIs—including OEE, availability, MTBF, MTTR, and downtime duration—typically via gauges or summary tiles 

  • Visualize downtime by reason—through Pareto charts or pie/bar graphs, showing which causes (e.g., breakdowns, material shortages, changeovers) dominate.

  • Offer timeline views—like Total Production Timeline™, shift-level charts or interactive Gantt-style visuals to spot patterns and correlations with shifts or events.

  • List detailed events—tables listing each downtime incident with start time, duration, machine, category, and notes 

🛠️ How It Integrates

  • Automated data capture from PLCs, OPC UA, MT Connect, and shop-floor systems 

  • Manual operator input supplements reason coding and root-cause details when required 

  • Alerts and thresholds notify teams in real time when downtime exceeds configured limits or shifts

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