A new methodology, called the Improved Bayesian (IB) method, for diagnosing equipment partial failures in process plants is described. The partial failure of an equipment unit generally implies a partial loss of its function(s). A partial failure can show different symptoms when it occurs, according to the level (i.e. failure strength) at which the function is lost, and this fact, in turn, makes the diagnosis even more difficult. Among diagnostic inference techniques, it has been shown that the Bayesian method is a theoretically superior method. The Bayesian method’s construction is based on a rigorous probabilistic interpretation of data and expert judgment. The Bayesian method is adopted in many diagnostic applications, and produces reasonable results in most cases. However, the assumption of independence of symptoms, normally employed in applying the Bayesian method to process malfunction diagnosis, can lead to erroneous conclusions. This problem can become worse when large numbers of partial failures are involved in the diagnosis. A subsidiary model called the F-curve model is developed in an effort to apply the Bayesian method more accurately in diagnosing partial failures. The F-curve model utilizes the knowledge of the symptom variation with respect to failure strength, hence visualizing the degree of adverse influence (i.e. severity) of a failure on the process. When the Bayesian method is modified by the F-curve model, it is referred to as the Improved Bayesian (IB) method. An application example is presented to verify that the proposed IB method can yield more accurate results than the Bayesian method in diagnosing partial failures.