Vibration signal analysis using symbolic dynamics for gearbox fault diagnosis

Medina, Rubén; Macancela, Jean Carlo; Lucero, Pablo; Cabrera, Diego; Cerrada, Mariela; Sánchez, René–Vinicio; Vásquez, Rafael E.

doi:10.1007/s00170-019-03858-0

Cited by 25 publications

(18 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The set of features based on SD is also simple to calculate and provides a high classification accuracy. This set of features has also been used for classifying faults in gearboxes using SVM classifiers [26] providing high accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…When dealing with a dynamical system as previously described, the surface Σ of the phase space can be partitioned into r disjoint subsets denoted p = {P 1 , P 2 , P 3 , ..., P r }. Each subset is represented by a symbol r ∈ {1, 2, 3, ..., r} = S. An orbit in the phase space is represented by a sequence of visited symbols s = {s 1 , s 2 , s 3 , ..., } [26]. The surface can be arbitrarily subdivided into several cells, and the points of the Poincaré plot located in each cell can be represented by the same symbol.…”

Section: Symbolic Dynamicsmentioning

confidence: 99%

“…This paper uses the SD feature extraction algorithm in [26], which is summarized in the following steps: (1) The Poincaré plot is traced using a lag of 15, (2) The phase-space represented by the Poincaré plot is subdivided into several angular regions as shown in Figure 14, (3) Each region is assigned to a symbol represented by an integer number, 24 angular regions are considered in this application, (4) The symbolic representation of the vibration signal is processed for obtaining the histogram. This is simply attained by counting the number of points in each angular region.…”

Section: Symbolic Dynamicsmentioning

confidence: 99%

See 2 more Smart Citations

Reciprocating Compressor Multi-Fault Classification Using Symbolic Dynamics and Complex Correlation Measure

et al. 2020

Self Cite

View full text Add to dashboard Cite

Prognostics and Health Management technologies are useful for early fault detection and optimization of reliability in mechanical systems. Reciprocating compressors units are commonly used in industry for gas pressurization and transportation, and the valves in compressors are considered vulnerable parts susceptible to failure. Then, early detection of faults is important for avoiding catastrophic accidents. A feasible approach for fault detection consists in measuring the vibration signal for extracting useful features enabling fault detection and classification. In this research, a test-bed composed by two-stage reciprocating compressor was used for simulating a set of 13 different conditions of combined faults in valves and roller bearings. Three accelerometers were used for collecting the vibration signals for extracting three different types of features. These features were analyzed furthermore by using two random forest models to classifying the different faults. The first set of features was obtained by applying the symbolic dynamics algorithm, which provides the histogram of a set of symbols. This set of symbols was obtained by subdividing a 2D Poincaré plot into angular regions and counting the intersection of the phase trajectories on each of regions. The second type of features corresponds to the complex correlation measure which is calculated as the addition of the areas of triangles belonging to a Poincaré plot. Additionally, a small set of classical statistical features was also used for comparing their classification abilities to the new set of proposed features. The three sets of features enable highly accurate classification of the set of faults when used with random forest classification models. Notably, the ensemble subspace k-Nearest Neighbors algorithm provides classification accuracies higher than 99%.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Symbolic Dynamicsmentioning

confidence: 99%

Section: Symbolic Dynamicsmentioning

confidence: 99%

See 1 more Smart Citation

Reciprocating Compressor Multi-Fault Classification Using Symbolic Dynamics and Complex Correlation Measure

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…It can be seen from the figure that the system enters a steady state after about 0.2 s. The average angular velocity is 12000 °/s and the range of fluctuation of the speed is less than 100 °/s, which satisfies the characteristics of the periodic meshing impact of the gear and conforms to the display. In order to reduce the interference of other vibration source, the vibration sensor is generally placed at the input and output ends of the gearbox in the actual working environment [37,38]. Therefore, the vibration signal of the output gear is collected in the Adams simulation environment, and the acceleration time domain signal and the frequency domain signal are shown in Fig.…”

Section: Fault Simulationmentioning

confidence: 99%

A novel wind turbine gearbox fault diagnosis method based on ASO-VMD and NRF

Shan¹,

Liu²

2020

J. vibroeng.

View full text Add to dashboard Cite

The combination of feature extraction and pattern recognition can make it possible to realize wind turbine gearboxes based on vibration signals. However, these methods need to be constantly adjusted parameters and spend time training when processing different vibration signals, which is time-consuming. Aiming at reducing the number of parameters that need to be adjusted and training time, this paper proposes a variational mode decomposition (VMD) based on atomic search optimization (ASO) and neural random forest (NRF) fault diagnosis model. The parameters of the VMD are adaptively adjusted by the ASO, which has the advantages of less adjustment parameters. After ASO-VMD decomposition, signals will be used as the input of NRF. We evaluate our method on simulation gearbox model which is established by Solidworks and Adams. Experimental results show that our method has faster training speed and higher recognition accuracy without set many parameters manually.

show abstract

“…However, either of WT and EMD seemingly encounters some difficulties in analyzing complex machinery vibration signals [9][10][11][12]. A lot of references have indicated that vibration signals from defective rotating machinery display obvious fractal and chaotic properties [1,[13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Feature extraction based on generalized permutation entropy for condition monitoring of rotating machinery

2021

View full text Add to dashboard Cite

Defective rotating machinery usually exhibits complex dynamic behavior. Therefore, feature representation of machinery vibration signals is always critical for condition monitoring of rotating machinery. Permutation entropy (PeEn), an adaptive symbolic description, can measure complexities of signals. However, PeEn may lack the capability to fully describe dynamics of complex signals since compressing all the information into a single parameter. Afterwards, multiscale PeEn (MPeEn) is put forward for coping with nonstationarity, outliers and artifacts emerging in complex signals. In MPeEn, a set of parameters serve to describe dynamics of a complex signal in different time scales. Nonetheless, an average procedure in MPeEn may withhold local information of a complex signal. To overcome deficiencies of PeEn and MPeEn, this paper proposes generalized PeEn (GPeEn) by introducing different time lags and orders into PeEn. In GPeEn, a complex signal is compressed into one matrix rather than a single parameter. Moreover, minimal, maximal and average values of a matrix obtained by GPeEn serve to briefly describe conditions of rotating machinery. Next, a numerical experiment proves that GPeEn outperforms PeEn and MPeEn in characterizing conditions of a Lorenz model. Subsequently, the performance of GPeEn is benchmarked against that of PeEn and MPeEn by investigating gear and roll-bearing vibration signals containing different types and severity of faults. The results show that the proposed method has a clear advantage over PeEn and MPeEn in condition monitoring of rotating machinery.

show abstract

Vibration signal analysis using symbolic dynamics for gearbox fault diagnosis

Cited by 25 publications

References 53 publications

Reciprocating Compressor Multi-Fault Classification Using Symbolic Dynamics and Complex Correlation Measure

Reciprocating Compressor Multi-Fault Classification Using Symbolic Dynamics and Complex Correlation Measure

A novel wind turbine gearbox fault diagnosis method based on ASO-VMD and NRF

Feature extraction based on generalized permutation entropy for condition monitoring of rotating machinery

Contact Info

Product

Resources

About