Application of Bispectrum Diagonal Slice Feature Analysis in Tool Wear States Monitoring

Tool wear is unavoidable during machining, which is one of the most common tool failure modes. It is significant to evaluate the tool state quickly and effectively for timely tool change strategy. The cutting vibration signals after tool wear show strong non-Gaussian characteristics. Higher order spectrum is a powerful tool for analyzing the non-Gaussian characteristics of signals, and can restrain noise and provide more information than classical power spectrum analysis. This paper presents a milling tool wear state monitoring method based on higher order spectrum entropy. Due to the large amount of calculation of bispectrum, bispectrum diagonal slice is investigated. And the diagonal slice spectral entropy is proposed as tool wear indicator to monitor tool state. To verify the proposed method, cutting vibration signal of CNC machining center were collected and analyzed. The experimental results showed that the proposed approach can effectively monitor and diagnose the tool state, and has good robustness. It is feasible and effective for on-line monitoring milling tool wear.

Wind Turbine Bearing Fault Detection Using Adaptive Resampling and Order Tracking

Wind energy is growing increasingly popular in the United States, so it is imperative to make it as cost competitive as possible. Operations and Maintenance (O&M) make up 20-25% of the total cost of onshore wind projects. Unplanned maintenance contributes approximately 75% of the total maintenance costs (WWEA, 2012). Condition-based maintenance strategies intend to maximize the uptime by reducing to the amounts of unplanned maintenance. This should result in an overall decrease in the cost of maintenance. Wind turbines produce an interesting challenge, because their main shaft rotation is both slow and nonstationary. Through the use of adaptive resampling and order tracking, both of these challenges were combated as the bearing fault was identified in the order spectrum then tracked as it progressed. The fault was identified as an outer race defect on the main bearing that initiated sometime during or before installation. The total energy in the order spectrum around the bearing fault rate was identified as a potential front-runner for a prognostic parameter. This paper presents a case study application to operational wind turbine bearing data to demonstrate the ease and intuitiveness of combining adaptive resampling and order tracking to diagnose faults for slow, nonstationary bearings. Prognosis of remaining useful life is proposed with features extracted from the order spectrum, but additional data are needed to develop and demonstrate this analysis.

Investigation on Energy Loss in Centrifugal Pump Based on Entropy Generation and High-Order Spectrum Analysis

Full-open, straight-blade centrifugal pumps have the advantages of simple structure and high stability at high speeds. However, this type of centrifugal pump has a large internal energy loss and low efficiency. The flow loss caused by unstable flow accounts for most of the internal energy loss of a centrifugal pump, and this energy dissipates mainly to the surroundings of the pump in the form of pressure pulsation and vibration. In this study, to effectively understand the hydraulic behavior and energy loss in a centrifugal pump, the energy losses in the pump were numerically analyzed by the entropy generation method, the distribution of the energies of pressure pulsation and vibration was evaluated based on experimental results at different flowrates, and the correlation between flow and energy loss at 20% of the design flowrate (Qd) was investigated by high-order spectrum analysis. The energies of pressure pulsation and vibration in the experiment were evaluated by the root-mean-square (RMS) value. The distribution of the entropy generation rate (EGR) indicated large energy losses at the diffusion section of the volute, clearance, tongue, and blade inlet. At 20% of the design flowrate (Qd), the energy loss was caused by the backflow near the tongue, and the EGR at EP2 had a nonlinear fundamental characteristic frequency of 0.44fn. By bispectrum and coherence analysis, the nonlinear fundamental characteristic frequencies generated by the unforced flow were analyzed, and it was found that unforced flow was transmitted to one another in the near-tongue region.

Seismic geologic structure characterization using a high-order spectrum-coherence attribute

Characterization of seismic geologic structures, such as describing fluvial channels and geologic faults, is significant for seismic reservoir prediction. The coherence algorithm is one of the widely used techniques for describing discontinuous seismic geologic structures. However, precise coherence attributes between adjacent seismic traces are difficult to compute due to the nonstationary and non-Gaussian property of seismic data. To describe seismic geologic structures accurately, we define a high-order spectrum-coherence (HOSC) attribute. First, we have developed a time-frequency (TF) analysis method to compute a constant-frequency seismic volume with high TF resolution, i.e., the second-order synchrosqueezing wave packet transform. Then, we developed a coherence approach by combining the mutual information (MI) calculation and coherence algorithm based on the eigenvalue computation (C3). To improve computational efficiency, we adopt the information divergence instead of the eigenvalue calculation of the C3-based algorithms. By applying our coherence algorithm to constant-frequency seismic volumes, we obtain the HOSC attribute. To test the validity of the proposed workflow, we evaluate the HOSC attribute using synthetic data. After applying our workflow to 3D real seismic data located in eastern China, the HOSC attribute characterizes seismic geologic discontinuities and subtle features clearly and accurately, such as fluvial channels and subtle faults.