This article explains what an Insight is, how to interpret the analysis panel, and how to log the check to close the investigation cycle. Use this guide if you're a technician, supervisor, or reliability engineer who needs to validate an anomaly detected by Tractian's AI.
Before you start
To perform an Insight check, you need:
Technician permission or higher on the platform.
To have completed a physical inspection of the asset (or the equivalent technical validation). Do not run the check without a prior inspection, since this compromises traceability and the quality of the AI model.
Access to the Insights module.
Important: The check is the only formal record that closes the Insight cycle. Changing the status manually does not replace the check.
What is an Insight
An Insight is an alert generated automatically by the platform when sensors detect an anomaly in an asset's behavior. When the system identifies a deviation in vibration, temperature, or another monitored parameter, it creates an Insight containing the detected failure mode, the quantitative anomaly data, and a prescribed action.
The Insight does not replace the maintenance team's technical assessment. It delivers the data and the initial diagnosis; failure confirmation is done through the check.
Each Insight presents the following information:
Field | What it indicates |
Failure mode | Type of detected anomaly (e.g., Mechanical Looseness, Bearing Wear, Unbalance) |
Criticality | Severity level of the anomaly |
Date and time | Moment when the anomaly was detected by the system |
Asset and location | Affected equipment and area |
Quantitative diagnosis | Measured values compared to the limits configured for the asset |
Prescription | Inspection checklist or recommended corrective action |
Insight types generated by AI
The AI detects anomalies through spectral analysis, machine learning models, and limits configured per asset. The main Insight types are described below.
Insight type | What it detects | Analyzed signal |
Bearing Wear | Characteristic bearing failure frequencies (BPFO, BPFI, BSF, FTF) with amplitude above the baseline | Acceleration envelope |
Unbalance | High amplitude at 1× RPM frequency, indicating unbalanced mass on the rotor | Radial RMS velocity |
Classic Misalignment | 1× and 2× RPM harmonics with high axial component, typical of angular or parallel misalignment | Axial + radial velocity |
Mechanical Looseness | Subharmonics (0.5×, 1.5×) and multiple RPM harmonics, indicating structural or mounting looseness | Velocity spectrum |
Lubrication Issue | Diffuse high-frequency energy (>3000 Hz) without defined peaks, indicating insufficient or degraded lubrication | High-frequency acceleration |
Electrical Issue | Peaks in the 3700 to 4200 Hz range related to the inverter (VFD) switching frequency | Spectral acceleration |
Temperature, Upper Limit | Asset temperature exceeds the configured critical limit | Sensor thermistor |
Gear Issue | Gear Mesh Frequency (GMF) and its harmonics with sidebands spaced at 1× RPM | Acceleration + envelope |
General Anomaly (ML) | Vibration pattern diverges significantly from the learned baseline, with no specific failure mode identified | Anomaly model |
💡 Tip: Detection is based on spectral analysis and the limits configured for the asset, not directly on Crest Factor or K Factor indicators. These indicators are informational and complement the analysis.
Insight panel structure
When you open an Insight, the side panel presents the following sections:
Section | What it shows | How to use it |
1. General Information | Name, number, asset, location, linked inspection, current status | Identify the equipment and the alert context |
2. Measured Values | Crest Factor, Defect Factor, K Factor, Peak to Peak | Assess severity and failure stage |
3. Detection | Parameter that triggered the alert, measured value, % variation vs. baseline, limit, axis, date/time | Understand exactly what the AI detected |
4. Prescription | Inspection checklist customized for the failure mode | Guide you during the physical inspection |
5. History | Charts showing parameter evolution over time | Verify failure progression or stabilization |
6. Harmonics | Multiple harmonics of the failure frequency (e.g., BPFO1 to BPFO4) | Confirm the characteristic pattern of the failure mode |
7. Spectral Analysis | Insight spectrum compared to the average in alert and to the training spectrum (baseline), available in 2D and 3D | Identify peaks and anomalous patterns |
8. Comments and History | Notes, attached files, mentions of team members | Log information during the investigation |
Metrics in the Measured Values section
Metric | What it represents | When it's relevant |
Crest Factor | Ratio between signal peak and RMS value. High values indicate impacts. | Early detection of bearing failures |
Defect Factor | Indicator of a localized defect on the component | Confirmation of a point defect |
K Factor | Sensitive to the early stages of bearing wear, before peaks become visible in the spectrum | Very early detection (pre-spectral) |
Peak to Peak | Total signal amplitude between the highest and lowest captured value | Indicator of severe shocks and impacts |
Insight status
The status indicates which stage of the analysis flow the Insight is in. It can be changed manually as the investigation progresses, following the flow:
Pending → Under Inspection → Awaiting Check → Check
Status | What it means |
Pending | The Insight was generated but has not yet been assessed |
Under Inspection | An inspection has been started to investigate the anomaly |
Awaiting Check | The inspection was performed physically, but formal validation on the platform has not been completed |
Check | The inspection was completed and is awaiting the recording of findings on the platform |
Important: Changing the status manually does not replace the formal check. To record the analysis result, create or link an Event, and close the Insight cycle, performing the check is mandatory.
How to perform the check
The check is the step where you record the analysis result, validate the diagnosis, and close the Insight cycle. It can be done with AI support (automatic assessment) or filled in manually.
Open the Insight. In the top menu, click Insights. Find the Insight you want in the list and click it to open the side panel.
Start the check flow. With the Insight panel open, click the Check button to start the flow.
Describe the inspection findings. Fill the text field with the inspection conclusions. The more detailed the description, including photos, videos, or audio, the more accurate the AI-generated assessment will be.
Choose the assessment mode. Select one of the two options:
a. Generate assessment: the AI analyzes the description provided and automatically suggests the assessment type, failure mode, issue status, and corresponding action.
b. Fill in manually: you select the assessment type directly and fill in the required fields.
Review and save. Review the data shown (in automatic mode) or fill in the requested fields (in manual mode). Click Save to record the check.
When you finish, the Insight will have an updated status, the assessment recorded in its history, and, when applicable, an Event created or linked automatically.
Automatic assessment with AI
When you select Generate assessment, Tractian uses artificial intelligence to analyze the description provided and automatically generate a structured assessment. The auto-assessment can suggest:
Assessment type: Potential Failure, Functional Failure, Process Adjustment, among others.
Identified failure mode and affected component, when applicable.
Issue status: Resolved or Unresolved.
Action that will be performed when saving: Event creation or linking.
This information is shown for review before confirmation. Adjustments made to the automatic assessment are used as feedback to continuously improve the analysis models.
Assessment types
After the physical inspection, select the assessment type that best describes the result. Each type has different implications for the Insight cycle and for AI learning.
Potential Failure
Failure mode identified but not yet impacting operation. The asset begins to be monitored more closely.
The Insight remains active.
It can be linked to an existing Event or create a new one.
Tracking is done through the Reliability tab.
The Insight is added to the Watchlist.
Functional Failure (Immediate action)
Failure mode confirmed and impacting the asset's function. Requires detailed completion.
Identified failures: type, component, cause, effect.
Location or asset where the failure occurred.
Status: Unresolved (with start date/time) or Resolved (with period, downtime, and repair time).
Generated savings can be recorded.
Process Adjustment (Operational)
Observed behavior related to operational changes, not equipment failure.
Examples: load change, processed material, operating regime.
Provide: cause of the adjustment, event period, affected asset or location.
The Insight is archived after the check.
Sensor Changes
Alert caused by physical interference with the sensor: repositioning, removal, or reinstallation.
The Insight is archived after the check.
No maintenance Event is generated.
Log the description for the record.
Asset Replacement
Full replacement of the asset or its main components has been performed.
Log the replaced component.
Links to the reliability history.
The Insight is archived after the check.
No Failures Found (Normal)
Normal operation confirmed after inspection. No failure or process change identified.
The Insight is archived.
Logging a detailed description of the verification is recommended.
The history remains available for consultation.
Automatic Event linking
If an unresolved Event already exists for the same asset, the platform may automatically link it to the Insight during the check, depending on the assessment type selected. In these cases, some of the information is then managed directly through the Event, and some edits become restricted until the Event is resolved.
✅ Check result: after saving, the assessment is recorded, an Event may be created or linked automatically, all actions are kept in the Insight's history, and created Events can be tracked through the Reliability tab.
Impact of the check on AI
The check is not just an operational record. It is also a training signal for the platform's artificial intelligence models. Each assessment feeds back into the system in different ways.
Assessment type | Impact on AI | Effect on the model |
Potential Failure | Confirms that the detected pattern is relevant | Reinforces the detection threshold for that failure mode on that asset |
Functional Failure | Confirms a real failure with identified component and cause | Stronger training signal; improves diagnosis and prescription accuracy |
Process Adjustment | Indicates the variation was operational, not a failure | Helps the AI distinguish process changes from real anomalies; reduces false positives |
Sensor Changes | Indicates physical interference with the sensor | Excludes the period from the training baseline; prevents corrupted data from contaminating the model |
No Failures Found | Indicates a false positive | Negative feedback; helps calibrate sensitivity and reduce unnecessary alerts |
Edited automatic assessment | Human correction of the AI suggestion | Direct quality feedback; used to continuously improve the auto-assessment model |
Important: checks with vague descriptions or incorrect classifications degrade model quality over time. The more detailed and accurate the check, the better the AI learns to distinguish real failures from operational noise.
Learning period (baseline)
The training spectrum, used as a healthy condition reference, is generated after the sensor's learning period (typically 30 days after installation). Checks performed during this period are especially important to ensure the baseline reflects normal equipment operation.
Decision tree for classification
Use this guide to determine the correct assessment type based on the inspection result.
Inspection result | Assessment type | Action |
Failure identified, but asset still operating normally | Potential Failure | Add to the Watchlist for monitoring |
Failure confirmed and impacting the asset's function | Functional Failure | Fill in component, cause, effect, and period |
Variation caused by an operational change (load, material, regime) | Process Adjustment | Provide cause and period |
Sensor was removed, repositioned, or reinstalled | Sensor Changes | The Insight will be archived |
Asset or main component was replaced | Asset Replacement | Log the replaced component |
Inspection performed, no failure or change found | No Failures Found | Describe the verification performed |
Heads up, do not check without inspecting: the classification must reflect the result of a real physical inspection or technical validation. Checking an Insight without a prior inspection compromises traceability and the quality of the AI model.
Frequently asked questions
Does the Insight disappear after being checked?
No. The Insight remains in the history with the updated status and all check records. Insights assessed as "No Failures Found" are archived, but can still be consulted.
Can I link an Insight to a maintenance Event?
Yes. During the check, you can create a new Event or link to an existing Event for the asset. If there's already an unresolved Event, the platform can make this link automatically.
The spectral analysis is blank. What should I do?
The training spectrum is generated after the sensor's learning period (about 30 days). If the asset was installed recently, wait for the baseline period to finish. If the sensor has been installed for more than 30 days and the analysis still doesn't appear, contact Tractian support.
Can I change the Insight status without performing the check?
Yes, the status can be changed manually at any time. However, changing the status does not record the analysis result and does not create or link Events. To formally close the Insight cycle, the check is mandatory.
What permission is needed to check an Insight?
To change the status of an Insight or perform the check, you need Technician permission or higher on the platform.
Can the AI's automatic assessment be wrong?
Yes. The automatic assessment is a suggestion based on the description provided. Always review the data shown before saving. If needed, click Edit to adjust any information. Corrections made are used as feedback to improve the model.
If the issue persists or you have questions about the correct classification, contact Tractian support.