Review of recent advances in the application of the wavelet transform to diagnose cracked rotors

Wavelet transform (WT) has been used in the diagnosis of cracked rotors since the 1990s. At present, WT is one of the most commonly used tools to treat signals in several fields. Understandably, this has been an area of extensive scientific research, which is why this paper aims to summarize briefly the major advances in the field since 2008. The present review considers advances in the use and application of WT, the selection of the parameters used, and the key achievements in using WT for crack diagnosis.The authors would like to thank the Spanish government for financing through the CDTI project RANKINE21 IDI-20101560

On the energy leakage of discrete wavelet transform

The energy leakage is an inherent deficiency of discrete wavelet transform (DWT) which is often ignored by researchers and practitioners. In this paper, a systematic investigation into the energy leakage is reported. The DWT is briefly introduced first, and then the energy leakage phenomenon is described using a numerical example as an illustration and its effect on the DWT results is discussed. Focusing on the Daubechies wavelet functions, the band overlap between the quadrature mirror analysis filters was studied and the results reveal that there is an unavoidable tradeoff between the band overlap degree and the time resolution for the DWT. The dependency of the energy leakage to the wavelet function order was studied by using a criterion defined to evaluate the severity of the energy leakage. In addition, a method based on resampling technique was proposed to relieve the effects of the energy leakage. The effectiveness of the proposed method has been validated by numerical simulation study and experimental study

Experimental diagnosis of multiple faults on a rotor-stator system by fast Fourier transform and wavelet scalogram

This paper presents the recent application of the scalogram of Continuous Wavelet Transform (CWT) as a vibration monitoring and signal processing tool for a rotor dynamic response under parametric excitation. The experimental test data of coupled lateral-torsional vibrations of a rotor-stator system with transverse crack was obtained through a data acquisition set-up interfaced with Rotor-Kit-4 (RK-4). Analysis was executed on rotor deflection, orbit, frequency and time-frequency spectrum of the RK-4 experimental data. The scalograms of CWT were used experimentally to represent the aperiodic occurrence of rub between the rotor-stator and crack features. Variation in 3-D scalogram peaks in the presence of rub and crack were unique and were used to distinguish quasi-periodic motion from other types of motion. An unbalanced cracked rotor gave a higher frequency amplitude response compared to an unbalanced rotor with rub under the same conditions. Irregularities in orbit orientation near sub-harmonic resonances were observed in the test data. Multiple rebounds inside the orbit loop were unique rub indicators. Conspicuous horizontal components of the higher harmonics were observed near the critical speed when a crack existed. CWT established inherent feature patterns that discriminated unbalance, rub and a crack

Analysis of the influence of crack location for diagnosis in rotating shafts based on 3 x energy

The aim of condition monitoring is to detect faults before a catastrophic failure occurs. Cracks in rotating shafts are especially critical. The present work studies vibration signals obtained from a rotating shaft under different crack depths and locations. Tests were performed in a rig called Rotokit at a steady state at different rotation speeds. Signals obtained are analyzed by means of energy using the Wavelet theory, specifically the Wavelet Packets Transform. Nine crack depths in the shafts were tested, from 4% to 50% of the shaft diameter. Previous related work showed good reliability for crack diagnosis using 3 x energy for cracks in the middle section. In the present work, previous results are compared to the obtained for a crack in a change of section at one side. In both crack locations, large changes in energy are observed at 3 x at high speeds. Energy levels at this harmonic were used for the inverse process of crack detection, and probability of detection curves were calculated by thresholding. Cracks with depths above 12% can be detected with reliability in the locations tested using this method.The authors would like to thankthe Spanish Government for financing through the projects RANKINE21 IDI-20101560 and MAQ-STATUS DPI2015-69325-C2-1-R

Crack detection in rotating shafts based on 3x energy: analytical and experimental analyses

Maintenance is essential to prevent catastrophic failures in rotating machinery. A crack can cause a failure with costly processes of reparation, especially in a rotating shaft. In this study, the wavelet transform theory was applied to vibration signals to detect cracks in a rotating shaft. Data were obtained from an analytical Jeffcott rotor model with a breathing function to simulate cracks. Large changes in energy when a crack appears were discovered at 1 ×, 2 × and 3 ×. Thereafter, vibration signals were obtained from a rotating machine at different steady-state rotational speeds using an accelerometer mounted on the bearing housing. Nine defect conditions were induced in the shaft (with depths from 4% to 50% of the shaft diameter). By matching the theoretical results with the experimental data, it was found that only the 3 × component of the rotational speed is a clear indicator of the presence of a crack in this case. The energy level at this harmonic can be used for the inverse process of crack detection. Moreover, “probability of detection” curves were calculated. They showed very good results.The authors would like to thank the Spanish Government for financing through the CDTI project RANKINE21 IDI-20101560.Publicad

A review of intelligent methods for condition monitoring and fault diagnosis of stator and rotor faults of induction machines

Nowadays, induction motor (IM) is extensively used in industry, including mechanical and electrical applications. However, three main types of IM faults have been discussed in the literature, bearing, stator, and rotor. Importantly, stator and rotor faults represent approximately 50%. Traditional condition monitoring (CM) and fault diagnosis (FD) methods require a high processing cost and much experience knowledge. To tackle this challenge, artificial intelligent (AI) based CM and FD techniques are extensively developed. However, there have been many review research papers for intelligent CM and FD machine learning methods of rolling elements bearings of IM in the literature. Whereas there is a lack in the literature, and there are not many review papers for both stator and rotor intelligent CM and FD. Thus, the proposed study's main contribution is in reviewing the CM and FD of IM, especially for the stator and the rotor, based on AI methods. The paper also provides discussions on the main challenges and possible future works

Automatic condition monitoring system for crack detection in rotating machinery

Maintenance is essential to prevent catastrophic failures in rotating machinery. A crack can cause a failure with costly processes of reparation, especially in a rotating shaft. In this study, the Wavelet Packets transform energy combined with Artificial Neural Networks with Radial Basis Function architecture (RBF-ANN) are applied to vibration signals to detect cracks in a rotating shaft. Data were obtained from a rig where the shaft rotates under its own weight, at steady state at different crack conditions. Nine defect conditions were induced in the shaft (with depths from 4% to 50% of the shaft diameter). The parameters for Wavelet Packets transform and RBF-ANN are selected to optimize its success rates results. Moreover, ‘Probability of Detection’ curves were calculated showing probabilities of detection close to 100% of the cases tested from the smallest crack size with a 1.77% of false alarms.The authors would like to thank the Spanish Government for financing through the CDTI project RANKINE21 IDI-20101560