Dynamic analysis of integrally geared compressors with varying workloads

Integrally geared compressors are characterized by compact and high efficiency machines, which are widely used in modern processing industries. As an important part of integrally geared compressors, a geared rotor-bearing system exhibits complicated dynamic behaviors. When running at rated speeds, a coupling system likely produces resonance with an adjusted workload, and a critical load phenomenon occurs. The dynamic coefficients of bearings, axial force and torque, and gear meshing stiffness vary with workload because of the interaction between rotors. In this study, a dynamic model of a geared rotor-bearing system influenced by the dynamic coefficients of bearings, axial force and torque, and gear meshing stiffness is developed. The dynamic responses of the coupling system are calculated and analyzed by using a typical five-shaft integrally geared compressor as an example. The effects of different parameters on the dynamic behaviors of the proposed system are also considered in the discussion. The geared rotor-bearing system is further investigated to examine the failure mechanism of the critical load

The potential biomarkers in predicting pathologic response of breast cancer to three different chemotherapy regimens: a case control study

<p>Abstract</p> <p>Background</p> <p>Preoperative chemotherapy (PCT) has become the standard of care in locally advanced breast cancer. The identification of patient-specific tumor characteristics that can improve the ability to predict response to therapy would help optimize treatment, improve treatment outcomes, and avoid unnecessary exposure to potential toxicities. This study is to determine whether selected biomarkers could predict pathologic response (PR) of breast tumors to three different PCT regimens, and to identify a subset of patients who would benefit from a given type of treatment.</p> <p>Methods</p> <p>118 patients with primary breast tumor were identified and three PCT regimens including DEC (docetaxel+epirubicin+cyclophosphamide), VFC (vinorelbine/vincristine+5-fluorouracil+cyclophosphamide) and EFC (epirubicin+5-fluorouracil+cyclophosphamide) were investigated. Expression of steroid receptors, HER2, P-gp, MRP, GST-pi and Topo-II was evaluated by immunohistochemical scoring on tumor tissues obtained before and after PCT. The PR of breast carcinoma was graded according to Sataloff's classification. Chi square test, logistic regression and Cochran-Mantel-Haenszel assay were performed to determine the association between biomarkers and PR, as well as the effectiveness of each regimen on induction of PR.</p> <p>Results</p> <p>There was a clear-cut correlation between the expression of ER and decreased PR to PCT in all three different regimens (<it>p </it>< 0.05). HER2 expression is significantly associated with increased PR in DEC regimen (<it>p </it>< 0.05), but not predictive for PR in EFC and VFC groups. No significant correlation was found between biomarkers PgR, Topo-II, P-gp, MRP or GST-pi and PR to any tested PCT regimen. After adjusted by a stratification variable of ER or HER2, DEC regimen was more effective in inducing PR in comparison with VFC and EFC regimens.</p> <p>Conclusion</p> <p>ER is an independent predictive factor for PR to PCT regimens including DEC, VFC and EFC in primary breast tumors, while HER2 is only predictive for DEC regimen. Expression of PgR, Topo-II, P-gp, MRP and GST-pi are not predictive for PR to any PCT regimens investigated. Results obtained in this clinical study may be helpful for the selection of appropriate treatments for breast cancer patients.</p

Multi-Factor Operating Condition Recognition Using 1D Convolutional Long Short-Term Network

For a diesel engine, operating conditions have extreme importance in fault detection and diagnosis. Limited to various special circumstances, the multi-factor operating conditions of a diesel engine are difficult to measure, and the demand of automatic condition recognition based on vibration signals is urgent. In this paper, multi-factor operating condition recognition using a one-dimensional (1D) convolutional long short-term network (1D-CLSTM) is proposed. Firstly, a deep neural network framework is proposed based on a 1D convolutional neural network (CNN) and long short-Term network (LSTM). According to the characteristics of vibration signals of a diesel engine, batch normalization is introduced to regulate the input of each convolutional layer by fixing the mean value and variance. Subsequently, adaptive dropout is proposed to improve the model sparsity and prevent overfitting in model training. Moreover, the vibration signals measured under 12 operating conditions were used to verify the performance of the trained 1D-CLSTM classifier. Lastly, the vibration signals measured from another kind of diesel engine were applied to verify the generalizability of the proposed approach. Experimental results show that the proposed method is an effective approach for multi-factor operating condition recognition. In addition, the adaptive dropout can achieve better training performance than the constant dropout ratio. Compared with some state-of-the-art methods, the trained 1D-CLSTM classifier can predict new data with higher generalization accuracy

A Study of Probabilistic Diagnosis Method for Three Kinds of Internal Combustion Engine Faults Based on the Graphical Model

A strategy for increasing the accuracy rate of internal combustion engine (ICE) fault diagnosis based on the probabilistic graphical model is proposed. In this method, a three-layer network with inference of probability is constructed, and both the material conditions and the signals collected from different engine parts are considered as the inputs of the system. Machine signals measured by sensors were processed in order to diagnose potential faults, which were presented as probabilities based on the components in layer 1, fault categories in layer 2, and fault symptoms in layer 3. The diagnosis model was built by using nodes and arcs, and the results depended on the connections between the fault categories and symptoms. The parameters of the network represented quantitative probabilistic relationships among all layers, and the conditional probabilities of each type of fault and relevant symptoms were summarized. Fault cases were simulated on a 12-cylinder diesel engine, and three fault types that often occur on ICEs were tested based on five different fault symptoms with different loads, respectively. The diagnostic capability of the method was investigated, reporting high accuracy rates

Dynamic Finite Element Model Updating Based on Correlated Mode Auto-Pairing and Adaptive Evolution Screening

A method for dynamic finite element (FE) model updating based on correlated mode auto-pairing and adaptive evolution screening (CMPES) is proposed to overcome difficulties in pairing inaccurate analytical modal data and incomplete experimental modal data. In each generation, the correlated mode pairings (CMPs) are determined by modal assurance criterion (MAC) values and the symbiotic natural frequency errors, according to an auto-pairing strategy. The objective function values constructed by correlated and penalized subitems are calculated to screen the better individuals. Then, both the updating parameters and the CMPs can be adjusted adaptively to simultaneously approach the ideal results during the iteration of population evolution screening. Three examples (a thin plate with small holes, an F-shaped structure, and an intermediate case with multi-layer thin-walled complex structure) were presented to validate the accuracy, effectiveness, and engineering application potential of the proposed method

Weak Fault Feature Extraction Scheme for Intershaft Bearings Based on Linear Prediction and Order Tracking in the Rotation Speed Difference Domain

Because both the inner and outer rings rotate, the intershaft bearings used in gas turbines do not have fixed bearing housings. As a result, the vibration of intershaft bearings cannot be measured directly. Therefore, a vibration signal can only be collected through indirect measurement. First, it must be transferred to adjacent bearings through the shafting. Then, it should be transferred by the elastic supports and complex structure of the thin-walled strut. The vibration signal is severely weakened during transmission under the influences of the transfer path. In the meantime, in the vibration of other components, a huge amount of noise is produced by the air flow, and the variable speeds of the inner and outer rings of the intershaft bearings make it harder to analyze the signal. Hence, it is very difficult to extract the vibration fault features of intershaft bearings. To deal with the variable speed of dual rotors, as well as the weak signal, a fault feature extraction scheme for the weak fault signals of intershaft bearings is proposed in this paper. This scheme is based on linear prediction, spectral kurtosis, and order tracking in the rotation speed difference domain. First, a prewhitening process, based on linear prediction, is applied to the fault signal of the intershaft bearings to eliminate the stationary component. Thus, the remaining components, including the impulse signal of faulty bearings and nonstationary noise, can retain the features of the vibrational bearings, in addition to reducing the noise. Second, the optimal center frequency and bandwidth of the band-pass filter, applied to resonant demodulation, are selected by spectral kurtosis. Subsequently, the enveloped signal containing the features of the faults found in the intershaft bearings is obtained by resonance demodulation. The quasi-stationary signal in the angle domain is acquired by the even angle resampling of the nonstationary envelope signal, as a result of the variable speed. The final order spectrum is obtained through a Fourier transform. Fault diagnosis can be conducted for the intershaft bearings by comparing this spectrum with the feature order of the bearing fault. Experiments were conducted to verify the validity of the proposed scheme

A SVDD and K-Means Based Early Warning Method for Dual-Rotor Equipment under Time-Varying Operating Conditions

Under frequently time-varying operating conditions, equipment with dual rotors like gas turbines is influenced by two rotors with different rotating speeds. Alarm methods of fixed threshold are unable to consider the influences of time-varying operating conditions. Hence, those methods are not suitable for monitoring dual-rotor equipment. An early warning method for dual-rotor equipment under time-varying operating conditions is proposed in this paper. The influences of time-varying rotating speeds of dual rotors on alarm thresholds have been considered. Firstly, the operating conditions are divided into several limited intervals according to rotating speeds of dual rotors. Secondly, the train data within each interval is processed by SVDD and the allowable ranges (i.e., the alarm threshold) of the vibration are determined. The alarm threshold of each interval of operating conditions is obtained. The alarm threshold can be expressed as a sphere, whose controlling parameters are the coordinate of the center and the radius. Then, the cluster center of the test data, whose alarm state is to be judged, can be extracted through K-means. Finally, the alarm state can be obtained by comparing the cluster center with the corresponding sphere. Experiments are conducted to validate the proposed method

Fault Diagnosis of Internal Combustion Engine Valve Clearance Using the Impact Commencement Detection Method

Internal combustion engines (ICEs) are widely used in many important fields. The valve train clearance of an ICE usually exceeds the normal value due to wear or faulty adjustment. This work aims at diagnosing the valve clearance fault based on the vibration signals measured on the engine cylinder heads. The non-stationarity of the ICE operating condition makes it difficult to obtain the nominal baseline, which is always an awkward problem for fault diagnosis. This paper overcomes the problem by inspecting the timing of valve closing impacts, of which the referenced baseline can be obtained by referencing design parameters rather than extraction during healthy conditions. To accurately detect the timing of valve closing impact from vibration signals, we carry out a new method to detect and extract the commencement of the impacts. The results of experiments conducted on a twelve-cylinder ICE test rig show that the approach is capable of extracting the commencement of valve closing impact accurately and using only one feature can give a superior monitoring of valve clearance. With the help of this technique, the valve clearance fault becomes detectable even without the comparison to the baseline, and the changing trend of the clearance could be trackable