In industrial rotating machinery, the transient signal usually corresponds to the failure of a primary element, such as a bearing or gear. However, faced with the complexity and diversity of practical engineering, extracting the transient signal is a highly challenging task. In this paper, we propose a novel time–frequency analysis method termed the transient-extracting transform, which can effectively characterize and extract the transient components in the fault signals. This method is based on the short-time Fourier transform and does not require extended parameters or a priori information. Quantized indicators, such as Renyi entropy and kurtosis, are employed to compare the performance of the proposed method with other classical and advanced methods. The comparisons show that the proposed method can provide a much more energy-concentrated time–frequency representation, and the transient components can be extracted with a significantly larger kurtosis. The numerical and experimental signals are used to show the effectiveness of our method.

A Concentrated Time–Frequency Analysis Tool for Bearing Fault Diagnosis

Review for order reduction based on proper orthogonal decomposition and outlooks of applications in mechanical systems

Order tracking (OT), which is realized by signal sampling in equal-angle increment according to the measured rotating speed, is a powerful technique for rotating machine fault diagnosis under variable-speed condition. However, if the tachometer cannot be installed on the rotating machine or the speed signal is not available for some reasons, OT is difficult to be realized. This review summarizes recent advances in the development of tacholess speed estimation methods for OT with its applications to fault diagnosis. First, the basis of rotating speed estimation and OT is revisited. Then, the methods are categorized into three groups including vibration or sound signal, electrical motor current signal, and video stream according to the signal source from which the speed is estimated. The principle, implementation procedures, key techniques, along with the merits, and shortcuts of these methods are summarized and discussed in detail. Afterward, a contrastive case study using three kinds of methods is provided to intuitively illustrate the performances of OT along with the applications in motor bearing fault diagnosis. A bibliography of the recent publications related to this topic is also provided to facilitate the selection and improvement of the tacholess OT methods in fault diagnosis applications. Finally, the research prospects are discussed.

Tacholess Speed Estimation in Order Tracking: A Review With Application to Rotating Machine Fault Diagnosis

The impulse features in a condition monitoring (CM) signal usually imply the occurrence of a defect in a rotating machine. To accurately capture the impulse components in a CM signal, a concentrated time-frequency analysis (TFA) method based on time-reassigned synchrosqueezing transform (TSST) is proposed. First, the limitation of the TSST method in dealing with strong frequency-varying signals is explored. Second, an iteration procedure is introduced to address the blurry time frequency representation problem of TSST. The convergence of the iteration algorithm is also analyzed. Finally, an algorithm is proposed to extract the impulse features for signal reconstructions, which are also useful for an accurate diagnosis of the fault type. A simulated noise-contaminated signal and three sets of experimental data are employed in this article to evaluate the performance of the proposed method. Results obtained from this article confirm that the proposed method has a better performance in dealing with impulsive-like signals than other TFA methods.

Time-Reassigned Multisynchrosqueezing Transform for Bearing Fault Diagnosis of Rotating Machinery

High-order synchrosqueezing wavelet transform and application to planetary gearbox fault diagnosis

Time-reassigned synchrosqueezing transform: The algorithm and its applications in mechanical signal processing

Vibration signal correction of unbalanced rotor due to angular speed fluctuation

An improvement of time-reassigned synchrosqueezing transform algorithm and its application in mechanical fault diagnosis

The invention discloses an automatic milling fluttering alarming threshold value setting method based on the 3 sigma rule. The method comprises the steps that firstly, the state information in the milling process is obtained; secondly, forced vibration frequency filtering is conducted on signals, and fluttering sensitive frequency band filtering is conducted on the signals; thirdly, the obtained signals are subjected to feature extraction, and fluttering identification indexes are selected according to needs and calculated; fourthly, the indexes are subjected to the normal distribution check through the normal distribution ration assuming checking method; fifthly, after the normal distribution check, the threshold value section [mu-3 sigma, mu+3 sigma] is set according to the 3 sigma rule, wherein mu is the average value of the fluttering indexes, and the sigma is the standard deviation of the fluttering indexes; and sixthly, fluttering identification is conducted, and the time when continuous three points of the fluttering identification indexes exceed the threshold value section serves as the fluttering alarming time. The reliability of milling fluttering identification is high, and the misdiagnosis rate and the diagnosis missing rate are reduced.

Automatic milling fluttering alarming threshold value setting method based on 3 sigma rule

The invention discloses a high-speed milling chatter on-line identification method based on an AR model. The method comprises the steps that (1) the state information of the milling process is acquired; (2) forced vibration frequency filtering is carried out on a signal; (3) chatter sensitive frequency band filtering is carried out on the signal; and (4) a model characteristic root index R(k) is constructed based on the difference of the AR model of the signal in a stable milling state and a chatter milling state, time varying AR(1) modeling is carried out on the signal in the stable milling process, and a recursive least square method with a forgetting factor is used for identification to obtain the variation of the model characteristic root R(k) of the model in the whole cutting process to identify chatter. Compared with a traditional chatter detection method, according to the method, characteristic information reflecting chatter and characteristic information irrelevant to the chatter are separated, and substantive characteristic parameters of a milling system are obtained; and the physical property of the milling chatter is represented substantially, sensitivity, precision and reliability of chatter detection are effectively improved, and the misdiagnosis rate and the missed diagnosis rate are decreased.

He Dong

Papers

Time-reassigned synchrosqueezing transform: The algorithm and its applications in mechanical signal processing

Vibration signal correction of unbalanced rotor due to angular speed fluctuation

An improvement of time-reassigned synchrosqueezing transform algorithm and its application in mechanical fault diagnosis

Automatic milling fluttering alarming threshold value setting method based on 3 sigma rule

High-speed milling chatter on-line identification method based on AR model