Ultrasonic thickness gauges (UTGs) are used to monitor corrosion and erosion in a wide variety of industrial assets, including pressure vessels, storage tanks, and ship hulls.
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Although this measurement process is well established and modern instruments are accurate and dependable, the value of a given reading depends on how it is used after the fact. Integrity engineers cannot use measurements that have been entered inaccurately, stored incorrectly, or lost, no matter how precise they were when taken.
The main challenges facing those performing ultrasonic thickness measurements (UTMs) do not come from the instruments themselves. Instead, they come from data management issues.
Why Is Thickness Data Important?
Thickness measurements provide essential information for assessing the condition and future performance of an industrial asset. These measurements reveal corrosion rates and inform remaining-life estimates, inspection-interval planning, and fitness-for-service assessments. The reliability of these assessments depends on the quality of the underlying dataset.
If a dataset includes incorrect readings, measurements attributed to incorrect locations, or missing results, the consequent calculations may be optimistic, overly conservative, or simply impossible to produce.
The Use of Manual Recording
Despite advances in inspection equipment, manual data recording remains common practice. Technicians will often record measurements on paper or on annotated drawings, then transfer these hard copies into spreadsheets or reporting templates.
There are practical reasons behind this approach. Paper records are easy to use, do not require training, and are suitable for more challenging work environments. However, every additional step of manual data transfer introduces another opportunity for error, data degradation, or data loss.
Where Can Data Integrity Fail?
There are multiple areas in which data integrity can be compromised.
Transcription Errors
Moving measurement data from one medium to another carries inherent risk. Numbers may be reversed, decimal points may be entered incorrectly, and handwritten values can be easily misinterpreted.
For example, a reading of 8.4 mm could easily be entered as 4.8 mm. If this error is not quickly identified, it could lead to unnecessary repairs and incur unnecessary costs. Since the errors occur during data handling rather than during measurement, ultrasonic gauges cannot detect them. This makes retrospective investigation difficult.
Inconsistent Location References
Reliable trend analysis depends on repeat measurements being taken at precisely the same points. As a result, thickness monitoring locations (TMLs) need clear, consistent identifiers that can be understood by different inspectors, often years apart.
Problems can arise when reference points are poorly defined or are only recorded on drawings that are no longer available. In these circumstances, readings can be difficult to compare, and the calculated corrosion rates become unreliable, potentially reflecting inconsistent positioning rather than real material loss.
Lost, Damaged, or Incomplete Records
Paper records can easily deteriorate through exposure to moisture and mold and require proper storage to mitigate the risk not only of physical damage but also of incorrect filing.
However, electronic records can also be vulnerable to similar problems. Spreadsheets stored on individual computers, circulated by email, or duplicated through multiple save versions can also result in fragmented records and datasets.
When inspectors cannot find baseline readings, establishing an asset's corrosion history can become extremely difficult, and records may need to be restarted, losing years of trend data in the process.
Missing Context and Weak Audit Trails
A thickness reading is only useful when it is accompanied by supporting information, such as the inspection date, the technician responsible, and the instrument, calibration status, and probe used.
Time pressures and administrative errors can cause these details to be lost from annual logs. Without this information and the ability to trace any changes made to the data, confidence in the dataset as a whole can be undermined, as auditors often want proof that the data is original, complete, and unaltered.
The Consequences of Data Integrity Breakdowns
The consequences of mismanaged data are widespread, including safety issues and increased costs.
Risks to Safety and Reliability
Thickness monitoring aims to prevent unplanned failures in industrial assets. For example, underreporting a wall loss or missing a deteriorating location due to incomplete records significantly weakens the safety margin an inspection is meant to provide.
Incomplete Audit and Compliance Findings
Inspection regimes based on codes such as API 510, 570, and 653, together with quality systems such as ISO 9001, require good record management. This means that inspection results must be retained, retrievable, and traceable.
Auditors often raise findings when:
- Records concerning an asset cannot be produced
- Readings cannot be linked to a location or technician
- They cannot verify a calculation
These issues can result in requests for corrective action and operational delays and, in some cases, even lead to an organization suffering reputational damage.
Financial and Operational Costs
If inspectors cannot trust historical data, it can increase workloads and expenditure in multiple areas. For example, conservative assumptions made on unreliable data may lead to premature repairs or more frequent inspections. Untrustworthy data can also complicate shutdown planning when an asset’s true condition cannot be ascertained.
What Does Good Practice Look Like?
Inspection techniques do not necessarily need to change to improve data integrity. Instead, it is more important that the measurement remains connected to its context and its associated record.
Good data management typically includes the following steps:
- Capturing data at the source: Recording readings directly into a digital system, where possible, reduces opportunities for errors that can arise during manual recording and transfer.
- Standardizing location identification: Applying a consistent naming convention for assets, components, and TMLs allows readings to be compared across inspections and inspectors.
- Recording context automatically: Collecting the date, operator, equipment, and calibration reference information at the same time as the reading improves the credibility and transparency of datasets.
- Maintaining a single, controlled repository: Centralizing storage and utilizing appropriate access permissions avoids excessive production of local copies and preserves historical information during personnel or contractor changes.
- Preserving a clear audit trail: This ensures edits also show what changed, who changed it, and when, saving original data from being overwritten without a trace.
- Automating calculations and reporting: Generating corrosion rates and remaining-life estimates should be calculated consistently from stored data rather than from manual reconstructions for each report.
These practices support expectations that records should be attributable, legible, original, and accurate. This framework is well established in regulated industries and is increasingly applied to industrial inspection, too.
The Role of Digital Tools
Digital data capture and reporting tools can help organizations apply these best-practice principles and create inspection records that can be located, trusted, and remain credible long after an inspection has taken place.
For example, Cygnus Instruments' CygLink software transfers gauge readings into a structured digital record that supports consistent reporting. It effectively reduces the need for manual record handling and eliminates many of the problems caused by analog recordkeeping.
Conclusion
Ultrasonic thickness gauging is an established method for monitoring material loss in industrial assets, but its measurements alone are not sufficient to ensure assets are being managed effectively. UTG datasets can only be trusted when they are managed properly: currently, manual logs, lost paperwork, and inconsistent location references frequently undermine corrosion trending, remaining-life assessments, and audit readiness.
By treating thickness data as a long-term asset and applying consistent processes for data capture, storage, and traceability, organizations can better manage risk, satisfy their auditors, and make confident, well-informed decisions about their plant.
This information has been sourced, reviewed, and adapted from materials provided by Cygnus Instruments Ltd.
For more information on this source, please visit Cygnus Instruments Ltd.