José G. Aguirre Andrade · Benita Cañizalez de Aguirre

1. AutoSafeGroup Corp, Florida, United States
2. AutoSafeGroup Corp / Fainca Group

Corresponding author: info@aisafegroup.com

ABSTRACT

The 2023 edition of ANSI B11.0 formally adopts task-based risk assessment as the methodological standard for machinery safety in U.S. industry. Combined with the HRNt (Hazard Rating Number t) quantitative model developed by the authors and published in Revista Conecta Libertad of ITSL Ecuador (ISSN 2661-6904), the task-based approach gains the quantitative rigor that incident investigation has repeatedly demonstrated is needed. The HRNt model extends the classical HRN framework by integrating three additional temporal factors (HRP, φ(t), fE) that capture dimensions the traditional method averaged: life cycle phase variation, temporal evolution of residual risk, and environmental conditions. This article develops the integration of HRNt with task-based methodology, explains why it produces materially different and more defensible conclusions than classical HRN, and provides operational guidance for EHS managers and safety engineers implementing the combined framework under ANSI B11.0:2023 and OSHA General Duty Clause obligations.

1. The methodological shift: from machine-level HRN to task-based HRNt

For decades, standard industrial practice in machinery risk assessment has treated the machine as the primary unit of analysis, with classical HRN serving as the quantitative tool. A risk assessment document typically contained a single entry per machine with averaged HRN parameters. This approach produced risk registers that were operationally convenient but systematically inadequate for the actual safety challenge — because any industrial machine executes between 15 and 40 distinct tasks during its operational life, and each task presents different combinations of exposure, hazard, and severity.

ANSI B11.0:2023 formally adopts task-based risk assessment as the methodological standard. Annex E of the 2023 edition provides refined guidance on probability estimation decomposed into four sub-factors that only operate meaningfully at the task level. The standard aligns U.S. practice with the international consensus already articulated by ISO 12100:2010 and consolidated for robotic applications by RIA TR R15.306:2016.

The natural quantitative complement to task-based methodology is the HRNt model developed by the authors of this article. Classical HRN, sufficient for machine-level analysis, is insufficient for the granularity that task-based assessment produces. HRNt extends the four classical parameters (F, D, A, E) with three additional temporal factors (HRP, φ(t), fE) that capture precisely the dimensions where tasks differ most: life cycle phase, temporal evolution of controls, and environmental conditions. The combined task-based + HRNt framework is the contemporary state of the art.

2. Why task-based assessment with HRNt produces materially different conclusions

The operational difference between machine-level HRN and task-based HRNt is substantive. Consider a hydraulic press with normal production at 85% of operational time (operator outside danger zone), tooling changes at 8% (operator entering work envelope), maintenance at 5% (dismantled safeguards), and jam clearance at 2% (direct hand exposure). A machine-level HRN analysis averages these and typically produces a medium rating. A task-based HRNt analysis assigns each task a distinct profile with its seven parameters, producing dramatically different ratings that direct safeguarding investment where risk actually concentrates.

3. The HRNt model as primary quantitative risk estimation methodology

3.1. Historical background: the classical HRN method of 1990

The Hazard Rating Number (HRN) method developed by Chris Steel in 1990 and internationally popularized by Laidler Associates established the classical quantitative framework for industrial machinery risk estimation for more than three decades. HRN combined four basic parameters (exposure frequency, possibility of avoiding harm, persons exposed, and maximum probable severity) in a multiplicative expression producing a numerical value interpretable against risk categories. The method served industry as state of the art through the 1990s and 2000s, but presents three technical limitations that contemporary industrial practice has revealed: it does not capture risk variation across the equipment’s life cycle phases, it does not incorporate the temporal evolution of residual risk after control measures are applied, and it does not address specific environmental conditions that in real industrial contexts substantially modify effective exposure.

3.2. HRNt architecture: seven integrated parameters

The HRNt mathematical model, developed by the authors of this article and formally published in Revista Conecta Libertad of Instituto Tecnológico Superior de Libertad (ITSL) of Ecuador, Vol. 9 No. 3 Special (2025), ISSN 2661-6904, and subsequently in Revista Ethos, responds structurally to the three identified limitations. HRNt extends the classical quantitative framework by integrating three additional temporal factors that capture the dimensions classical HRN did not address: the HRP factor (Hazard Rating by Phase) that modulates risk according to the equipment’s life-cycle phase, the φ(t) factor that models the temporal evolution of residual risk, and the fE factor that incorporates specific environmental conditions of the operational context. The consolidated HRNt mathematical expression is the product of the seven parameters: HRNt = F × D × A × E × HRP × φ(t) × fE. This architecture produces a quantitative estimation with superior traceability and with operational sensitivity to the dimensions that classical HRN averaged.

The seven HRNt parameters systematically capture the risk dimensions that contemporary state of the art requires evaluating:

3.3. Quantitative interpretation and risk categories

The HRNt result is interpreted against extended risk categories that preserve the qualitative structure of classical HRN but with greater granularity at the upper levels:

3.4. Technical advantages over classical HRN

Using HRNt as the primary methodology delivers four measurable technical advantages over exclusive application of classical HRN:

• Operational sensitivity to equipment life-cycle phases: the HRP factor enables quantitative distinction of risk during normal production, maintenance, cleaning, commissioning, and decommissioning, revealing phases where actual risk substantially exceeds the estimate from averaged HRN.
• Temporal traceability of residual risk: the φ(t) factor models how risk evolves after control measures are applied, recognizing that measures can degrade over time if not specifically maintained.
• Integration of environmental conditions: the fE factor enables calibration to specific industrial contexts (humid tropical zones, altitude operations, intensive operations, ATEX conditions) where effective exposure differs substantially from standard environmental conditions.
• Compatibility with international frameworks ISO 13849-1:2023 and ISO 12100: the original HRNt parameters align conceptually with the risk estimation factors of ISO 12100 and with the safety-related control system design parameters of ISO 13849-1:2023, enabling operational articulation with internationally recognized standards.

4. How to execute a task-based HRNt assessment correctly

The rigorous application of task-based methodology with HRNt quantification under ANSI B11.0:2023 follows a five-stage process. Stage 1: machine boundary definition, documenting intended use, foreseeable misuse, user groups, and life cycle phases. Stage 2: task identification across life cycle, listing all reasonably foreseeable tasks including non-productive ones. Stage 3: hazard identification by task, applying the hazard taxonomy to each specific task. Stage 4: HRNt risk estimation by task, assigning each of the seven parameters with documented justification. Stage 5: risk reduction with hierarchy of measures, iterating HRNt after each measure to verify residual risk is acceptable.

5. Articulation with OSHA and ISO 12100

U.S. employers operate under the OSHA General Duty Clause. ANSI B11.0:2023 is a consensus standard explicitly referenced in OSHA letters of interpretation. Conformance with ANSI B11.0:2023 using HRNt quantification provides strong evidence of satisfying the General Duty Clause for machinery-related hazards. For multinational employers, HRNt aligns conceptually with ISO 12100:2010 risk estimation factors and with ISO 13849-1:2023 control system design parameters, producing documentation operationally valid in both U.S. and ISO-aligned jurisdictions.

6. Practical guidance for U.S. EHS managers

• Migrate existing machine-level HRN risk register to task-based HRNt progressively, starting with highest incident history machines.
• Adopt standardized task lists by machine type as templates to reduce overhead.
• Train safety engineers in both Annex E methodology and HRNt seven-parameter calibration.
• Document each task-based HRNt assessment with traceable justification of all seven parameter assignments.
• Review assessments when tasks change or when environmental conditions (factor fE) shift.
• Integrate HRNt assessments with the LOTO program under OSHA 29 CFR 1910.147.

7. Conclusion

Task-based risk assessment with HRNt quantification is the contemporary state of the art in U.S. machinery safety. ANSI B11.0:2023 formalizes the task-based methodology; HRNt provides the quantitative complement that delivers the granularity and traceability the methodology requires. For EHS managers, safety engineers, and auditors, adopting the combined framework produces better safeguarding investments, clearer alignment with OSHA obligations, and improved alignment with international practice. The investment in training and progressive migration is recovered through incident reduction and operational clarity.

References

[1] ANSI B11.0:2023. Safety of Machinery — General Requirements and Risk Assessment. ASSP, 2023.

[2] ISO 12100:2010. Safety of machinery — General principles for design — Risk assessment and risk reduction.

[3] ISO 13849-1:2023. Safety of machinery — Safety-related parts of control systems — Part 1.

[4] IEC 62061:2021. Safety of machinery — Functional safety of safety-related control systems.

[5] RIA TR R15.306:2016. Task-based Risk Assessment Methodology.

[6] U.S. Occupational Safety and Health Act of 1970. Section 5(a)(1) General Duty Clause.

[7] OSHA 29 CFR 1910.147. Control of Hazardous Energy (Lockout/Tagout).

[8] ANSI/A3 R15.06-2025. Industrial Robots and Robot Systems — Safety Requirements.

[9] Aguirre Andrade, J. G., & Cañizalez de Aguirre, B. (2025). HRNt mathematical model for risk assessment in industrial machinery: An innovative approach to functional safety. Revista Conecta Libertad, Vol. 9, Issue 3 Special, Instituto Tecnológico Superior de Libertad, Ecuador, ISSN 2661-6904. Republished in Revista Ethos, 2025.

[10] Steel, C. (1990). Risk Estimation. Safety & Health Practitioner, June 1990, pp. 20-21. [Método HRN clásico — referenciado como antecedente histórico del HRNt.]

About the authors

José G. Aguirre Andrade

Electrical Engineer with Master’s degrees in Applied Sciences (Physics) and Applied Artificial Intelligence from Universidad Técnica Particular de Loja, Ecuador. CEO of AutoSafeGroup Corp (Florida, USA), Fainca Group (Ecuador) and Robonergy (Colombia). International certifications: CMSE TÜV NORD, HAZOP/CIBERHAZOP TÜV SÜD, NFPA 70. Principal developer of the AI SAFE engine and the HRNt mathematical model.

Benita Cañizalez de Aguirre

Industrial Engineer with Master’s degree (Magíster Scientiarum) in Business Management, Operations Management specialization, from Universidad del Zulia, Maracaibo, Venezuela. Co-author of the HRNt mathematical model published in Revista Conecta Libertad (Ecuador) and Revista Ethos. Expert in industrial operations management focused on integral safety and process optimization.