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A novel approach for systematically assessing the current feedback controller performance, diagnosing, and removing the root causes resulting in performance deterioration is proposed. This fault diagnosis methodology does not require the traditional complex physical model and/ or a prior external input to perturb the operating system in order to achieve accurate fault identification. This is achieved by a series of the statistical hypothesis procedure, testing an impulse response of the control output which includes the feedback invariant (FBI) and the feedback dependent (FBD) terms. Both term variances via the statistic hypothesis testing are performed by comparing the current control and benchmark operating conditions. If the current performance has significant discrepancy from the benchmarked one, then the just-identified fault element (the process dead time, the controller tuning, the process, or the unmeasured disturbance) is isolated first and then the same diagnostic procedure based on FBI and FBD for exploring the new possible fault is successively applied. According to the characteristics of the closed loop output response, a diagnosis tree structure that has the characters of hierarchy and integrated knowledge is established. It is organized based on the sequence from general to the special. The capability of the proposed method is illustrated through a simulation case and a level tank system, including the single and the multiple fault problems.