Application of KS test in ball bearing fault diagnosis

Kar, Chinmaya; Mohanty, A.R.

doi:10.1016/s0022-460x(03)00380-8

Cited by 49 publications

(23 citation statements)

References 6 publications

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“…In a two-sample KS test, the null hypothesis is that the two samples come from the same distribution. The statistic for deriving the comparison in KS test is known as D-stat, which has the following formula [31]:…”

Section: Benchmark With Distribution Comparison Techniquesmentioning

confidence: 99%

A Minimal-Sensing Framework for Monitoring Multistage Manufacturing Processes Using Product Quality Measurements

Ardakani

Lee

2018

Machines

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Abstract:For implementing data analytic tools in real-world applications, researchers face major challenges such as the complexity of machines or processes, their dynamic operating regimes and the limitations on the availability, sufficiency and quality of the data measured by sensors. The limits on using sensors are often related to the costs associated with them and the inaccessibility of critical locations within machines or processes. Manufacturing processes, as a large group of applications in which data analytics can bring significant value to, are the focus of this study. As the cost of instrumenting the machines in a manufacturing process is significant, an alternative solution which relies solely on product quality measurements is greatly desirable in the manufacturing industry. In this paper, a minimal-sensing framework for machine anomaly detection in multistage manufacturing processes based on product quality measurements is introduced. This framework, which relies on product quality data along with products' manufacturing routes, allows the detection of variations in the quality of the products and is able to pinpoint the machine which is the cause of anomaly. A moving window is applied to the data, and a statistical metric is extracted by comparing the performance of a machine to its peers. This approach is expanded to work for multistage processes. The proposed method is validated using a dataset from a real-world manufacturing process and additional simulated datasets. Moreover, an alternative approach based on Bayesian Networks is provided and the performance of the two proposed methods is evaluated from an industrial implementation perspective. The results showed that the proposed similarity-based approach was able to successfully identify the root cause of the quality variations and pinpoint the machine that adversely impacted the product quality.

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Section: Benchmark With Distribution Comparison Techniquesmentioning

confidence: 99%

A Minimal-Sensing Framework for Monitoring Multistage Manufacturing Processes Using Product Quality Measurements

Ardakani

Lee

2018

Machines

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“…It tends to be more sensitive around the median value, and less sensitive at the extreme ends of the distribution. Differentiating small changes is difficult and remains to be a major challenge [29]. One way to improve the performance of K-S test on the tails is to replace D by a so-called stabilized statistic, such as Kuiper's statistic [33], which is defined as…”

Section: Kolmogorov-smirnov Test and Kuiper Testmentioning

confidence: 99%

“…These data are used to assess the performance of the proposed WSN diagnosis method. [29]. AR model, together with K-S test, has also been proved to be able to perform bearing fault diagnosis and gear performance prognosis [30,31].…”

Section: Numerical Simulation Of Wsnmentioning

confidence: 99%

“…However, compared with the studies on the proactive approaches, the studies on passive diagnosis approaches for WSN are limited. Recent research findings show approaches based on non-parametric test and autoregressive (AR) model are useful in machine health monitoring [29][30][31]. As AR model is well known for being able to filter out faulty system affected signal effectively, and non-parametric test is also reliable for classifying different operation conditions, we in this paper propose a passive diagnosis approach based on non-parametric test and AR model for anomaly detection of WSNs.…”

Section: Introductionmentioning

confidence: 99%

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Kuiper test and autoregressive model-based approach for wireless sensor network fault diagnosis

et al. 2014

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Wireless sensor networks (WSNs) have recently received increasing attention in the areas of defense and civil applications of sensor networks. Automatic WSN fault detection and diagnosis is essential to assure system's reliability. Proactive WSNs fault diagnosis approaches use embedded functions scanning sensor node periodically for monitoring the health condition of WSNs. But this approach may speed up the depletion of limited energy in each sensor node. Thus, there is an increasing interest in using passive diagnosis approach. In this paper, WSN anomaly detection model based on autoregressive (AR) model and Kuiper test-based passive diagnosis is proposed. First, AR model with optimal order is developed based on the normal working condition of WSNs using Akaike information criterion. The AR model then acts as a filter to process the future incoming signal from different unknown conditions. A health indicator based on Kuiper test, which is used to test the similarity between the training error of normal condition and residual of test conditions, is derived for indicating the health conditions of WSN. In this study, synthetic WSNs data under different cases/conditions were generated and used for validating the approach. Experimental results show that the proposed approach could differentiate WSNs normal conditions from faulty conditions. At last, the overall results presented in this paper demonstrate that our approach is effective for performing WSNs anomalies detection.

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“…It is designed to test the null hypothesis in favor of the alternative hypothesis. Kar and Mohanty [20] In this study, the vibration signatures received from roller bearing are analyzed by the Analysis of Variance technique. This method allows consideration of probability to derive statistical inferences about the groups of observations.…”

Section: Related Literaturementioning

confidence: 99%

Fault Diagnosis of Shaft- Ball Bearing System Using One-Way Analysis of Variance

Yüzükırmızı

Arslan

2014

MCA

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Abstract-Roller bearing is one of the most widely used and critical elements in rotating machinery. In consequence, bearing fault diagnosis in machines, as well as to discriminate the different fault conditions have been a great interest. In this study, firstly, analytical model of a shaft-ball bearing system is developed. The shaft is assumed to be perfectly rigid and uniform, and supported by two radial ball bearings. Then, the effect of localized defects on bearing running surfaces (i.e. surfaces of inner and outer rings and balls) on the shaft vibrations are obtained using the simulation program. Then, vibration signatures are analyzed by one-way analysis of variance (ANOVA) method. Finally, post-hoc tests are applied to differentiate the ball bearing element's localized defects in shaft-ball bearing simulation model.

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Application of KS test in ball bearing fault diagnosis

Cited by 49 publications

References 6 publications

A Minimal-Sensing Framework for Monitoring Multistage Manufacturing Processes Using Product Quality Measurements

A Minimal-Sensing Framework for Monitoring Multistage Manufacturing Processes Using Product Quality Measurements

Kuiper test and autoregressive model-based approach for wireless sensor network fault diagnosis

Fault Diagnosis of Shaft- Ball Bearing System Using One-Way Analysis of Variance

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