Practical aspects of machine diagnostics accuracy
Abstract
In any activity connected with monitoring, diagnostics and prognostics, the accuracy and believability of results is of paramount importance. Two main kinds of diagnostic errors, called Type I and Type II or False Positives and False Negatives are encountered whenever classification, such as “good” vs “bad” is considered. This has been thoroughly discussed, especially in the field of medicine, where the consequences of false judgements can be particularly grave. Other disciplines highly interested in this topic are financial forecasting and insurance. Industrial equipment diagnostics deals with these issues as well. In this paper we consider various factors influencing the quality of data, upon which diagnostic classification is based. We also look at the consequences of diagnostic errors to demonstrate why they are bad. We consider various ways employed to mitigate both the probability and the consequences of false judgements.
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Inaccurate diagnostic test results primarily manifest as two error types: false positives and false negatives.
“Inaccurate diagnostic test results primarily manifest as two error types: false positives and false negatives [192].”
From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [192]
False positives can lead to unnecessary treatments, psychological distress, additional testing, and occasionally unwarranted invasive procedures.
“In some cases, false positives can lead to unnecessary treatments, psychological distress, additional testing, and occasionally unwarranted invasive procedures [192].”
From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [192]
False negatives harm patient outcomes and weaken trust in diagnostic accuracy, fostering a false sense of security and postponing essential interventions.
“This oversight harms patient outcomes and weakens trust in diagnostic accuracy, fostering a false sense of security and postponing essential interventions [192].”
From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [192]
False positives can lead to unnecessary examinations and treatments, while false negatives may result in missed diagnoses and intervention delays.
“False positives can lead to unnecessary examinations and treatments, while false negatives may result in missed diagnoses and intervention delays.”
From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [192]
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Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors
Yen, C., Epling, J. W., Rockwell, M., & Vaughn-Cooke, M. (2026) · Diagnostics · doi:10.3390/diagnostics16020347 · Cited by 6 · see citing works
- Authors
- Orkisz, M.
- Venue
- 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)
- Publisher
- IEEE
- Published
- 2017-08
- Pages
- 260-266
- OpenAlex
- W2762371162
- Cited by
- 1
- References
- 0
- Citations by year
- 2026: 1
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Keywords
- machine diagnostics, diagnostic accuracy, false positives, false-negative results, diagnostic errors, classification, data quality, prognostics, industrial equipment monitoring, error mitigation
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