Research techniques of prognostics for gas turbines anddiesel engines have advanced in recent years. An analysis oftrends in these techniques would benefit researchersassessing growth in the field and planning future researchefforts. Prognostics research techniques were identified in1,734 published papers dated 1997-2016 from both thePrognostics and Health Management (PHM) Society andpapers identified by CiteSeerx that were published at venuesother than the PHM Society. In order to categorize papers byresearch technique, a taxonomy of prognostics was created.Additionally, the papers were categorized into two topics:gas turbines and diesel engines. In a large proportion ofpapers, trends in research techniques of prognostics for gasturbines and diesel engines reflected improvements in thespeed of multi-core computer processors, the development ofoptimized learning methods, and the availability of largetraining sets. The variety of prognostics research techniquesthat were identified in this review demonstrated the growthin prognostics research and increased use of this knowledgein the field. This systematic analysis of trends in researchtechniques of prognostics for gas turbines and diesel enginesis useful to assess growth and utilization of knowledge in thelarger field, and to provide a rationale (i.e., strategy, basis,structure) for planning the most effective use of limitedresearch resources and funding.