To　address　the　challenges　of　extracting　features　from　complex　industrial　process　data,　the　reliance　of　numerous　fault　detection　methodologies　on　presupposed　data　distribution　types,　and　the　limited　generalization　capacity　of　fault　detection,　this　manuscript　introduces　a　sophisticated　algorithm　for　industrial　process　fault　detection.　This　algorithm　harnesses　the　information　gain　adaptive　(IGA)　technique　for　feature　selection　and　a　synergistic　model　decision　mechanism.　Initially,　the　process　involves　the　computation　of　information　gain　via　decision　trees,　coupled　with　the　determination　of　the　k$$　k　$$　value　through　cross-validation.　This　strategy　enables　the　adaptive　selection　of　features,　thereby　facilitating　data　dimensionality　reduction　and　effective　feature　extraction.　The　subsequent　phase　introduces　a　ternary　statistical　measure　monitoring　group　for　the　detection　of　linear　faults,　while　autoencoders　and　one-class　SVM　methodologies　are　applied　for　the　monitoring　of　nonlinear　faults.　The　culmination　of　this　approach　is　the　development　of　an　innovative　weighted　decision　mechanism,　designed　to　amalgamate　the　findings　from　both　linear　and　nonlinear　detection　avenues,　yielding　more　dependable　detection　results.　The　validation　of　this　algorithm　employs　datasets　from　the　water　chillers　process　and　Tennessee　Eastman　(TE)　process,　demonstrating　the　IGA-combined　model＇s　superior　performance　over　isolated　linear　or　nonlinear　detection　algorithms　in　terms　of　detection　accuracy　and　robustness.　Notably,　the　efficacy　of　this　method　is　not　contingent　upon　specific　assumptions　regarding　data　distribution,　rendering　it　a　versatile　and　efficacious　tool　for　the　fault　detection　in　industrial　processes.