S
Stephen Ogaji
Researcher at Cranfield University
Publications - 78
Citations - 2487
Stephen Ogaji is an academic researcher from Cranfield University. The author has contributed to research in topics: Turbofan & Artificial neural network. The author has an hindex of 25, co-authored 78 publications receiving 2306 citations.
Papers
More filters
Journal ArticleDOI
Designs of anaerobic digesters for producing biogas from municipal solid-waste
TL;DR: In this article, the authors provide a synthesis of the key issues and analyses concerning the design of a high-performance anaerobic digester for the production of biogas from municipal solid waste.
Journal ArticleDOI
Reducing the cost of preventive maintenance (PM) through adopting a proactive reliability-focused culture
TL;DR: The economic and political realities of the 1990s forced managers to reverse long-standing organizational cultures in order to reduce costs and energy expenditures in their organisations as discussed by the authors, and these can be achieved, with respect to maintenance, by replacing a reactive repair-focused attitude by a proactive reliability-focused culture.
Journal ArticleDOI
Implementing total productive maintenance in Nigerian manufacturing industries
TL;DR: In this paper, the authors explore the ways in which Nigerian manufacturing industries can implement TPM as a strategy and culture for improving its performance and suggest self-auditing and bench-marking as desirable prerequisites before TPM implementation.
Journal ArticleDOI
Bioremediation of a crude-oil polluted agricultural-soil at Port Harcourt, Nigeria
TL;DR: In this paper, a combination of treatments, consisting of the application of fertilizers and oxygen exposure, was evaluated in situ during a period of six weeks, where conditions of a major spill were simulated by sprinkling crude-oil on experimental cells containing agricultural soil.
Journal ArticleDOI
Gas-turbine diagnostics using artificial neural-networks for a high bypass ratio military turbofan engine
TL;DR: In this article, an engine diagnostic structure is proposed using several artificial neural networks (ANNs) to distinguish between single-component faults (SCFs) and double component faults (DCFs).