This Small Business Innovation Research Phase 1 project proposes to develop an advanced non-contact, non-destructive monitoring technology to reliably identify early fatigue damage in pipelines, piping, and tubing. Current industry practice relies on statistically based, semi-empirical models that unfortunately can have an order of magnitude in error in predicting fatigue life, a leading failure mode in metals that are exposed to high cycling, high strain, and/or elevated pressures and temperatures. The United States infrastructure relies on millions of miles of pipes, pipelines, and tubing that are critical in the transport of valuable fluids serving a number of industries including municipal water, production and transport of oil and gas, and chemicals in processing plants. Unfortunately, this aging infrastructure can fail with disastrous consequences. The successful development and commercialization of this new technology will be a great leap in integrity monitoring, resulting in significant reduction in costs and delays, and eliminating catastrophic failures in the field. The estimated total addressable market for pipeline and coil tubing monitoring are in excess of $4 billion annually.The intellectual merit of this SBIR project centers on the demonstration of the technical and economic feasibility of this novel fatigue monitoring technology with a specific target of identifying when the material has reached 90% of its actual fatigue life. The technology is based on a novel electromagnetic measurement scheme that can effectively detect magnetic dislocations in the material which have been shown to be early indicators of failure due to fatigue. Using advanced analytics, strong correlations between the measured electromagnetic properties and fatigue life can provide a direct and reliable identifier of imminent fatigue damage without loss of material integrity. The major challenges that this SBIR effort proposes to address are (1) engineering an electromagnetic-based measurement technique with sufficient sensitivity to detect the magnetic defects seen in early fatigue damage under field operating conditions (2) developing appropriate detection algorithms to handle the statistical variations affecting fatigue damage and (3) engineering a cost-effective monitoring solutions compared with alternative fatigue management practices.
