Industrial Asset Intelligence
Industrial assets operating in high-stakes environments cannot sustain performance through reactive maintenance. Effective asset reliability requires integrating engineering domain knowledge, condition monitoring, and analytical methods into coherent health management architectures that anticipate degradation before it produces downtime or failure.
My work in this area spans hands-on reliability engineering, physics-of-failure analysis, and the application of probabilistic and machine learning methods to asset health monitoring. It is grounded in direct operational experience managing tool fleets and investigating failures in the field, extended through the design and delivery of digital condition monitoring and predictive maintenance systems.
Case Studies
Selected examples illustrating asset health management across engineering, data, and operational contexts.
Fleet Reliability Transformation
Problem
A critical failure mode was causing a substantial proportion of a global downhole tool fleet to fail prematurely during operations. The failure was escalating, the root cause was not understood, and the combined financial and customer relationship impact was mounting without a clear path to resolution.
Approach
I took ownership of the problem as a cross-functional programme coordinating field operations, research, and engineering. High-frequency measurement data was integrated into the investigation for the first time, enabling the underlying physical failure mechanism to be identified. This insight drove targeted interventions across the fleet, including instrumentation upgrades, mechanical design changes, and revised engineering workflows, each validated through a controlled pilot campaign before fleet-wide deployment.
Impact
The failure mode was effectively eliminated. Tool losses fell substantially, operational performance in affected cases improved significantly, and the work received formal recognition as a technical award for engineering depth and commercial contribution.
Risk-Based Asset Selection
Problem
Equipment selection decisions for complex operations relied on reliability assumptions embedded in engineering criteria that had never been validated against observed field performance at scale. Some assets were being deployed in conditions where their actual failure risk was materially higher than assumed.
Approach
Probabilistic models were built using large historical operational datasets, producing failure risk profiles for each asset type based on actual environmental exposure rather than nominal specifications. This allowed the reliability assumptions in existing selection criteria to be tested empirically and corrected where they diverged from observed performance.
Impact
Incorrect reliability assumptions were identified across a subset of the asset portfolio. Selection criteria and risk management protocols were updated accordingly, establishing a data-driven foundation for condition-informed asset selection decisions.