Fault detection via sparsity-based blind filtering on experimental vibration signals

Detection of bearing faults is a challenging task since the impulsive pattern of bearing faults often fades into the noise. Moreover, tracking the health conditions of rotating machinery generally requires the characteristic frequencies of the components of interest, which can be a cumbersome constraint for large industrial applications because of the extensive number of machine components. One recent method proposed in literature addresses these difficulties by aiming to increase the sparsity of the envelope spectrum of the vibration signal via blind filtering (Peeters. et al., 2020). As the name indicates, this method requires no prior knowledge about the machine. Sparsity measures like Hoyer index, l1/l2 norm, and spectral negentropy are optimized in the blind filtering approach using Generalized Rayleigh quotient iteration. Even though the proposed method has demonstrated a promising performance, it has only been applied to vibration signals of an academic experimental test rig. This paper focuses on the real-world performance of the sparsity-based blind filtering approach on a complex industrial machine. One of the challenges is to ensure the numerical stability and the convergence of the Generalized Rayleigh quotient optimization. Enhancements are thus made by identifying a quasi-optimal filter parameter range within which blind filtering tackles these issues. Finally, filtering is applied to certain frequency ranges in order to prevent the blind filtering optimization from getting skewed by dominant deterministic healthy signal content. The outcome proves that sparsity-based blind filters are effective in tracking bearing faults on real-world rotating machinery without any prior knowledge of characteristic frequencies.

Comparing Different Filter-Parameter for Pre-Processing of Brain-Stimulation Evoked Motor Potentials

Background: Brain stimulation motor-evoked potentials (MEPs) are transient signals and not periodic signals, and thus, they differ significantly in their properties from classical surface electromyograms. Unsuitable pre-processing of MEPs due to inappropriate filter settings leads to distortions. Filtering of extensor carpi radialis MEPs with transient signal characteristics of 20 subjects was examined. The effects of a 1st-order Butterworth high-pass filter (HPF) with different cut-off frequencies 1 Hz, 20 Hz, 40 Hz, and 80 Hz and a 5 Hz Butterworth high-pass filter with degrees 1st, 2nd, 4th, 8th-order are investigated for the filter output. Results: The filtering of the MEPs with an inappropriate filter setting led to distortions on the parameters peak-to-peak amplitudes of the MEP (MEPpp) and the absolute integral of the MEP (MEParea). The lowest distortions of all of the examined filter parameters were revealed after filtering with the lowest filter order and the lowest cut-off frequency. The 1st-order 1 Hz HPF calculation results in a difference of −0.53% (p < 0.001) for the MEPpp and −1.94% (p < 0.001) for the MEParea. Significance: Reproducibility is a major concern in science, including brain stimulation research. Only the filtering of the MEPs with appropriate filter settings led to mostly undistorted MEPs.

Filtration of histogram evaluation of probability density based on fuzzy data accessibility to a grouping interval

The paper proposes a histogram estimate of the probability density based on fuzzy data belonging to a grouping interval. A methodology for constructing a histogram estimate using a histogram smoothing filter is presented. The technique of constructing such a filter is described. The main filter parameter is established – the coefficient of the statistical relationship between the amount of data falling into the grouping interval for a single inclusion function and when approaching to use the membership function. The use of an iterative procedure for a histogram filter allows for a greater “smoothness” of the histogram. The simulation results show the effectiveness of using a histogram filter for different data volumes. At the same time, the choice of the number of grouping intervals for the “correct” recognition of probability density becomes not critical. The histogram filter is a simple tool that can easily be built into any algorithm for constructing histogram estimates.

Adaptive Predictive Control with Neuro-Fuzzy Parameter Estimation for Microgrid Grid-Forming Converters

Model predictive control (MPC) is a flexible and multivariable control technique with better dynamic performance than linear control. However, MPC is sensitive to parametric mismatches that reduce its control capabilities. In this paper, we present a new method of improving the robustness of MPC to filter parameter variations/mismatches by easily implementable parameter estimation. Furthermore, we extend the proposed technique for wider operating conditions by novel neuro-fuzzy estimation. The results, which are demonstrated by both simulations and real-time hardware-in-the-loop tests, show a steady-state parameter estimation accuracy of 95%, and at least 20% improvement in total harmonic distortion (THD) than conventional non-adaptive MPC under parameter mismatches up to 50% of the nominal values.

An efficient predictive current controller with adaptive parameter estimation in 3-ϕ inverter

In this paper, a detail design and description of a predictive current control scheme are adopted for three-phase grid-connected two-level inverter and its application in wind energy conversion systems. Despite its advantages, the predictive current controller is very sensitive to parameter variations which could eventually affected on system stability. To solve this problem, an estimation technique proposed to identify the value of harmonic filter parameter based on Model reference adaptive system (MRAS). Lyapunov stability theory is selected to guarantee a robust adaptation and stable response over large system parameter variation. The simulation results shows the efficiency of the proposed techniques to improve the current tracking performance.

Particle Swarm Optimization Based Adaptive Kalman Filters for Fault Diagnosis of Reaction Wheels

There has been an increasing interest in fault diagnosis in recent years, as a result of the growing demand for higher performance, efficiency, reliability and safety in control systems. A faulty sensor or actuator may cause process performance degradation, process shut down, or a fatal accident. Quick fault detection and isolation can help avoid abnormal event progression and minimize the quality and productivity offsets. In space systems specifically, space and power are limited in the satellites, which means that hardware redundancy is not very practical. If actuator faults occur, analytical redundancy techniques should be employed to determine if, where, and how the fault(s) occurred. To do so, different approaches have been developed and studied and one of the wellknown approaches in the literature is using the Kalman Filter as an observer for the purpose of parameter estimation and fault detection. The gains for the filter should be selected and the selection of the process and measurement noise statistics, commonly referred to as “filter tuning,” is a major implementation issue for the Kalman filter. This process can have a significant impact on the filter performance. In practice, Kalman filter tuning is often an ad-hoc process involving a considerable amount of time for trial and error to obtain a filter with desirable –qualitative or quantitative- performance characteristics. This thesis focuses on presenting an algorithm for automation of the selection of the gains using an evolutionary swarm intelligence based optimization algorithm (Particle Swarm) to minimize the residuals of the estimated parameters. The methodology can be applied to any filter or controller but in this thesis, an Adaptive Unscented Kalman Filter parameter estimation applied to a reaction wheel unit is used for the purpose of performance evaluation of the proposed methodology.

Particle Swarm Optimization Based Adaptive Kalman Filters for Fault Diagnosis of Reaction Wheels

There has been an increasing interest in fault diagnosis in recent years, as a result of the growing demand for higher performance, efficiency, reliability and safety in control systems. A faulty sensor or actuator may cause process performance degradation, process shut down, or a fatal accident. Quick fault detection and isolation can help avoid abnormal event progression and minimize the quality and productivity offsets. In space systems specifically, space and power are limited in the satellites, which means that hardware redundancy is not very practical. If actuator faults occur, analytical redundancy techniques should be employed to determine if, where, and how the fault(s) occurred. To do so, different approaches have been developed and studied and one of the wellknown approaches in the literature is using the Kalman Filter as an observer for the purpose of parameter estimation and fault detection. The gains for the filter should be selected and the selection of the process and measurement noise statistics, commonly referred to as “filter tuning,” is a major implementation issue for the Kalman filter. This process can have a significant impact on the filter performance. In practice, Kalman filter tuning is often an ad-hoc process involving a considerable amount of time for trial and error to obtain a filter with desirable –qualitative or quantitative- performance characteristics. This thesis focuses on presenting an algorithm for automation of the selection of the gains using an evolutionary swarm intelligence based optimization algorithm (Particle Swarm) to minimize the residuals of the estimated parameters. The methodology can be applied to any filter or controller but in this thesis, an Adaptive Unscented Kalman Filter parameter estimation applied to a reaction wheel unit is used for the purpose of performance evaluation of the proposed methodology.

Real-Time Chatter Detection via Iterative Vold-Kalman Filter and Energy Entropy

Real-time chatter detection is important in improving the surface quality of workpieces in milling. Since the process from stable cutting to chatter is characterized by the progressive variation of the vibration energy distribution, entropy has been utilized to capture the decreasing randomness of vibration signals when chatter occurs. To make such an index more sensitive to transitions of the cutting state, the entropy can be computed based on signal components obtained through signal decomposition techniques. However, the classic empirical mode decomposition (EMD) is difficult to put into practice due to its weak robustness to noises. The up-to-date variational mode decomposition (VMD) has strict requirements on priori information of the signal and thus is not applicable either. In this paper, a novel method named the iterative Vold-Kalman filter (I-VKF) is proposed under the framework of the greedy algorithm, where the Vold-Kalman filter (VKF), a classic order-tracker for rotating machinery, is improved to recursively extract each signal component. In the meantime, a spectrum concentration index-based technique is developed for the instantaneous chatter frequency estimation to adaptively determine the filter parameter. Numerical examples demonstrate the superiority of the I-VKF over the original VKF, EMD, and VMD, especially in the presence of strong noises. Combined with the energy entropy of extracted components and an automatically calculated threshold, the proposed strategy greatly helps in timely chatter detection, which has been verified by dynamic simulation and experiments.