When a PLC connection drops or a sensor returns corrupt readings, you need to know immediately — before a dashboard shows the wrong number or a rule triggers a false alarm.
Proxus tracks five quality dimensions for every connected device. This page shows you what each dimension measures, how to read the scores, and what to do when quality drops below acceptable levels.
After reading this page, you will be able to:
- Interpret the 5 data quality scores in Device Details
- Understand what causes each score to drop
- Set up rules that react to quality drops
- Diagnose the most common causes of low freshness or accuracy
The 5 Dimensions of Data Quality
Every device in Proxus is continuously scored across five independent dimensions, each on a 0–100% scale.
| Dimension | What It Measures | Drops When… |
|---|---|---|
| Accuracy | Percentage of successful read cycles with complete data | PLC returns errors, partial reads, or protocol fails |
| Completeness | Percentage of expected tags that are actually received | Some tags return null or are missing from the response |
| Integrity | Percentage of received data that passes validation | Data types mismatch, values out of expected range, corrupt payloads |
| Consistency | Rolling window stability of recent success/failure | Intermittent connection drops, flapping device |
| Freshness | How recently the last data was received | Device goes offline, network outage, polling delay |
Reading the Scores Together
| Scenario | Accuracy | Freshness | What It Means |
|---|---|---|---|
| Healthy device | 95%+ | 95%+ | Everything is working normally |
| Network outage | 95% | 40% | Device was reading correctly but has not sent data recently — check gateway connectivity |
| PLC configuration error | 30% | 90% | Device is reachable but most read attempts fail — check address mapping and protocol settings |
| Damaged sensor | 80% | 95% | Reads are coming in but some values fail validation — inspect the physical sensor |
| Flapping connection | 60% | 70% | Intermittent drops affect both accuracy and freshness — check network stability |
Freshness Thresholds
Proxus calculates freshness using time-based interpolation. The score decays as data ages:
| Time Since Last Data | Freshness Score |
|---|---|
| ≤ 30 seconds | 100% |
| ≤ 1 minute | 95% |
| ≤ 2 minutes | 90% |
| ≤ 5 minutes | 80% |
| ≤ 15 minutes | 60% |
| ≤ 1 hour | 40% |
| ≤ 12 hours | 10% |
| > 24 hours | 0% (completely stale) |
For most industrial environments, a freshness score below 80% (data older than 5 minutes) warrants investigation. For safety-critical applications, set your alert threshold at 90% (2 minutes).
Device Diagnostic Metrics
Beyond the 5 quality dimensions, every device maintains detailed diagnostic counters:
| Metric | What It Tracks | Why It Matters |
|---|---|---|
| Total Requests | Total communication attempts | Baseline activity — if this stops increasing, the device is not being polled |
| Successful Requests | Successful communications | Compare with Total Requests to calculate success rate |
| Failed Requests | Failed communications | Rising count signals protocol or network issues |
| Last Communication Time | When the gateway last attempted to reach the device | If this is old, the gateway may be down |
| Last Data Received Time | When valid data was last received | If this is old but Last Communication is recent, the device is reachable but not returning data |
| Average Response Time | Average response time in ms | Increasing values suggest network congestion or overloaded PLC |
| Connection Error Count | Number of connection failures | High count = network or firewall issue |
| Read Error Count | Number of read operation failures | High count = wrong address mapping or protocol config |
You can view these metrics in Device Details.
What Happens When a Device Goes Offline
When a device disconnects:
- Freshness decays over time based on the threshold table above
- Accuracy reflects the last known success rate (it does not retroactively drop)
- Consistency decreases as the rolling window captures more failures
- The device status changes to Offline in the Operations Center
Proxus does not inject artificial "Bad" quality values for tags when a device is offline. Instead, the freshness score provides clear visibility into data staleness. The last known value remains available until new data arrives.
Using Quality in Automation
You can use data quality metrics in your Rules Engine logic:
Example: Create a rule that triggers an alert when any device's Data Freshness drops below 60% (no data for 15+ minutes):
- Navigate to Automation > Rules and create a new rule
- Set the condition to monitor the freshness metric
- Add an Alert Action to notify the operations team
- Assign the rule to the relevant gateways
This lets you catch failing devices before operators notice missing data on dashboards.
Common Quality Problems and Solutions
| Symptom | Check First | Solution |
|---|---|---|
| Freshness drops to 0% suddenly | Gateway status in Management Console | Gateway may have disconnected — see Gateway Issues |
| Accuracy drops gradually | Failed Requests count in Device Details | Review Protocol Issues for your protocol type |
| Completeness is low from the start | Device Profile tag mapping | Verify all tag addresses match the PLC configuration — use Tag Browser |
| Consistency fluctuates rapidly | Network stability between gateway and device | Check cabling, switch health, and IP conflicts |
| Integrity drops for specific tags | Data type configuration | Ensure the tag data type in the profile matches the PLC register type |
Where Quality Metrics Appear
- Device Details: Per-device breakdown of all 5 dimensions and diagnostic counters
- Operations Center: System-wide Data Freshness and Connectivity insights
- UNS Explorer: Quality indicators alongside live values
- REST API: Query device metrics programmatically for custom dashboards