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Chief Nuclear Inspector’s Themed Inspection of Ageing Management at Magnox Hinkley Point A Site

  • Site: Hinkley Point A
  • IR number: 21-113
  • Date: October 2021
  • LC numbers: N/A

Executive summary

Purpose of Intervention

To conduct an ageing management inspection of Magnox Limited (ML) Hinkley Point A (HPA) Site as part of the Chief Nuclear Inspector’s (CNI) themed inspection on the management of ageing assets across the Nuclear Industry. The inspection was informed through a previously conducted self-assessment carried out by ML. The CNI themed inspections are to seek assurance that sustainable programmes are implemented for the management of ageing assets against four themes: monitoring of ageing, organisational capability, obsolescence, and ongoing investment. The implementation of ageing management arrangements for both safety and security were inspected.

Interventions Carried Out by ONR

An ageing management inspection was not judged against any Licence Conditions (LC).

Plant walkdowns of the following areas were conducted during the inspection:

  • The East Side 1A Blower House
  • The Active Effluent Treatment Plant

The focus of the inspection was to establish if ML’s arrangements are adequately implemented to detect the onset of equipment degradation, ensuring the organisational capability to adequately manage ageing management, the identification and management of obsolescence and the ongoing investment in asset management. The inspection included discussions with various responsible members of responsible ML staff. The ONR inspection team was joined by ML’s EHSS&Q Site Inspectors (internal regulators).

Explanation of Judgement if Safety System Not Judged to be Adequate

Not applicable.

Key Findings, Inspector's Opinions and Reasons for Judgements Made

Overall, it was concluded that the arrangements for ageing management had been adequately implemented and good examples of ongoing efforts to improve ageing management were provided.

It was noted that the previous Parent Body Organisation (PBO) management model had affected ML’s ability to address ageing management at the HPA site. The site’s management team demonstrated its commitment to improve aspects of ageing management.

ML has a systematic strategy of identifying assets across the site and is using a risk-informed approach to prioritise asset management of key systems. Noting the difficulties presented by the previous PBO model, HPA has a significant number of systems requiring attention and a large backlog of defects. The site’s Plant Health Committee recently undertook an in-depth review to better understand the asset management requirements for all systems across the site with entry into deferral now anticipated to be 10-15 years away. This involved all system engineers and the site lead team; I judge this to be an area of good practice.

Maintaining resource levels at HPA to ensure organisational capability for effective ageing management has been a challenge under the old PBO model. ML has adequate processes in place to manage the baseline and key posts and recognise ongoing challenges in the workforce demographics. ML has approved the recruitment of a number of roles at HPA to improve the capability at the site. It was noted that the site would benefit from a defined role profile and training requirements for the Security Systems Engineer.

ML uses the same principles in its asset management process as for the management of obsolescence, with the strategy for the holding of spares for a given system linked to the potential consequences of failure. ML provided good examples of rationalisation of systems; an example was the simplification of electrical systems to manage obsolescence.

The previous PBO model led to a lack in investment in ageing management and a subsequent backlog in defects at HPA. ML now has systems and processes in place for ongoing investment in ageing management and HPA has been able to access funding to address some of the priority issues at the site. Corporately, ML has a good awareness of where the different sites in the fleet are with respect to ageing management.

Conclusion of Intervention

I judge from the evidence sampled that ML is effectively monitoring ageing and managing obsolescence at HPA site.