We have developed a novel fault identification approach based on injecting a predefined diagnostic signal into the system and then extracting fault-related features. The diagnostic signal is designed to enrich the fundamental response instead of generating a separable response. Meanwhile, the abundant dynamics ensure sensitivity to fault parameters. The chosen feature extraction technique is the PST (phase space topology) method, conceived and developed by our own team. The PST method extracts density-based features from the phase space density distributions, which circumvent frequency analysis and the influence of noise.

In this study, the electro-hydraulic servo actuator is chosen as the experimental setup, consisting of a symmetrical cylinder controlled by a two-stage servo valve, as shown in the figure below. Electro-hydraulic servo actuator systems generally consist of a torque motor and two stages of hydraulic power regulation. The working principle of the electro-hydraulic servo actuator can be summarized as follows: The electromagnetic torque motor controls the first stage by positioning a flapper. The flapper controls the hydraulic fluid flow through two nozzles in the first stage, providing positive internal closed-loop flow control. Next, differential pressure positions the second-stage spool, which controls the direction and rate of hydraulic fluid flow to the actuator. The figure above represents the functional schematic of an electro-hydraulic servo actuator.

For the identification of faults, we propose to inject a predefined signal into the servo-actuator system. A periodic signal was selected as an input, as follows.

The original excitation, which is the input current signal to the motor, is also assumed to be a periodic signal, as follows,

where 𝐴𝐼 and𝑓𝐼 are the amplitude and the frequency of the injected signal, respectively. The main idea here is to inject a signal at a specific frequency which can enrich the nonlinear response and unfold some hidden information about the system's health condition. This signal should not affect the usual operation of the system. The amplitude of the original excited signal was assumed as 𝐴𝑜=0.04, and the diagnostic signal amplitude was selected to be a smaller value 𝐴𝐼=0.01. Because the goal is to develop a diagnostic approach that will not cause harm to the system, The diagnostic signal frequency was selected to be 𝑓𝐼=800𝐻𝑧 while the input signal frequency was selected to be 𝑓𝑜=800𝐻𝑧. The displacement and velocity of the rotating angle of the motor armature are only excited by the original signal, and the results with both the original signal and diagnostic signal are demonstrated in the figures below.

Time series of angle displacement without and with diagnostic signal

Obviously, the diagnostic signal enriches the dynamics of the time responses, while the amplitude of the time response remains at the same level, ensuring the normal operation of the system for online diagnostics. In this case, we leverage the PST method to extract features for diagnostics, and simulation results demonstrate higher accuracy and capability in noise proofing.