A review of diagnostic methods shows that new techniques are required that quantify system degradation from measured response. Information theory, developed by Claude E. Shannon, involves the quantification of information defining limits in signal processing for reliable data communication. One such technique considers information theory fundamentals forming an analogy between a machine and a communication channel to modify Shannon`s channel capacity concept and apply it to measured machine system response. The technique considers the residual signal (difference between a measured signal induced by faults from a baseline signal) to quantify degradation, perform system health assessment, and diagnose faults. Similar to noise hampering data transmission, mechanical faults hinder power transmission through the system. This residual signal can be viewed as noise within the context of information theory, to permit application of information theory to machines to construct a health measure for assessment of machine health. The goal of this dissertation is to create and study metrics for assessment of machine health. This dissertation explores channel capacity which is grounded and supported by proven theorems of information theory, studies different ways to apply and calculate channel capacity in practical industry settings, and creates methods to assess and pinpoint degradation by applying the channel capacity based measures to signals. Channel capacity is the maximum rate of information that can be sent and received over a channel having a known level of noise. A measured signal from a machine consists of a baseline signal exemplary of health, intrinsic that contaminates all measurements, and signals generated by the faults. Noise, the difference between the measured signal and the baseline signal, consists of intrinsic noise and "fault noise". Separation between fault and intrinsic (embedded in the measurement) noise shows channel capacity calculations for the machine require minimal computational efforts, and calculations are consistent in the presence of intrinsic white noise. Considering the response average or DC component of a signal in the channel capacity calculations adds robustness to diagnostic results. The method successfully predicted robot failures. Important to system health assessment is having a good baseline response as reference. The technique is favorable for industry because it applies to measurement data and calculations are done in the time domain. The technique can be used in semi-conducting industry as a tool monitoring system performance and lowering fab operating cost by extending component use and scheduling maintenance as needed. With a window running average channel capacity the technique is able to locate the fault in time. / text