Monitoring of fuel oil process of marine diesel engine

Boullosa-Falces, David; Larrabe-Barrena, Juan Luis; López-Arraiza, Alberto; Menéndez, J.C Flórez; Solaetxe, Miguel Ángel Gómez

doi:10.1016/j.applthermaleng.2017.08.036

Cited by 19 publications

(15 citation statements)

References 16 publications

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“…Thus, R f is monitored and when it decreases, it is known that biofouling has occurred. Boullosa-Falces et al (2017) [ 23 ] plotted the different values of R f in a graph and table and showed the decrease in R f and in this way, visualized the results of applying this technique. Moreover, several other techniques to detect biofouling within heat exchangers have been investigated, examples of which are acoustic, x-rays, optical, and ultrasound.…”

Section: Technological Substratum Of Detection Techniquesmentioning

confidence: 99%

“…The negative aspects of these techniques are the costs associated with them [ 16 ]. Moreover, in earlier research by Boullosa-Falces et al (2017), the CUSUM method was applied to marine diesel engines to detect fluctuations in parameters [ 23 ]. These varying applications of CUSUM (in a heat exchanger and marine diesel engine) suggests that this technique is applicable in different areas such as bioreactors.…”

Section: Technological Substratum Of Detection Techniquesmentioning

confidence: 99%

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A Technological Understanding of Biofilm Detection Techniques: A Review

et al. 2020

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Biofouling is a persistent problem in almost any water-based application in several industries. To eradicate biofouling-related problems in bioreactors, the detection of biofilms is necessary. The current literature does not provide clear supportive information on selecting biofilm detection techniques that can be applied to detect biofouling within bioreactors. Therefore, this research aims to review all available biofilm detection techniques and analyze their characteristic properties to provide a comparative assessment that researchers can use to find a suitable biofilm detection technique to investigate their biofilms. In addition, it discusses the confluence of common bioreactor fabrication materials in biofilm formation.

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Section: Technological Substratum Of Detection Techniquesmentioning

confidence: 99%

Section: Technological Substratum Of Detection Techniquesmentioning

confidence: 99%

A Technological Understanding of Biofilm Detection Techniques: A Review

et al. 2020

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“…We have perturbed the data using small and moderate amounts of distortions (0.75σ and 1.4σ) and applied the proposed assorted and other classical charts covered in this study. The graphical and tabular representation of the resulting charts and their detection abilities are presented in Figures 8,9 and Table 12. From these results, it is obvious that the performance of the proposed assorted chart is better than the competing Shewhart, CUSUM, and EWMA control charts for to detect different amounts of shifts as may be seen from Table 12 and Figures 8,9. 6 | SUMMARY, CONCLUDING REMARKS, AND FUTURE RECOMMENDATIONS The classical Shewhart control chart is a memoryless control chart that is used to detect large shifts.…”

Section: Application Through Data Perturbation On Ph Characteristicmentioning

confidence: 99%

“…In literature, several classical and enhanced schemes are available to detect different amounts of shift in process location parameter. Some of these control charting strategies are as follows: fast initial response (FIR) CUSUM by Lucas and Crosier, FIR EWMA by Lucas and Saccucci, new CUSUM by Crosier, generalized weighted moving average control chart by Sheu and Lin, a run‐rules scheme by Abbas et al, mixed EWMA‐CUSUM (MEC) by Abbas et al, mixed CUSUM‐EWMA by Zaman et al, small sudden deviation method by Boullosa‐Falces et al, homogeneously weighted moving average control chart by Abbas, and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Assorted control charts: An efficient statistical approach to monitor pH values in ecotoxicology lab

Abbas

Saeed

2019

Journal of Chemometrics

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Primarily, three types of control charts are employed to observe the disturbances in the process parameters. The large shifts are detected efficiently by Shewhart's control chart, whereas for small and medium instabilities, cumulative sum and exponentially weighted moving average control charts are used. This study proposes an assorted approach to monitor small, medium, and large disturbances in a single control charting procedure. For the evaluation of the proposed assorted control chart, we have used various measures like average run length, standard deviation run length, extra quadratic loss, relative average run length, and performance comparison index. A comparison of the proposed assorted control chart is presented with some typical charts including the Shewhart, cumulative sum, exponentially weighted moving average control charts, and some advanced modification such as combined Shewhart‐EWMA (CSE), combined Shewhart‐CUSUM (CSC), mixed EWMA‐CUSUM (MEC), FIR EWMA, and adaptive EWMA (AEWMA). Finally, a real‐life application example is presented to monitor the pH level of water in an ecotoxicology lab.

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“…However, few researches have been devoted to apply this technique into diesel engine fault diagnosis. The typical researches on this topic can be listed as: Boullosa took advantage of Hotelling's T 2 statistic control charts to detect the cylinder lubrication process and fuel oil process of a marine diesel engine [13,14]. Wang introduced the local statistical approach into the nonlinear statistical process control technique, and Hotelling's T 2 and Q statistics were produced to detect the air leaks of an automotive diesel engine [15].…”

Section: Introductionmentioning

confidence: 99%

A Multivariate Statistics-Based Approach for Detecting Diesel Engine Faults with Weak Signatures

Zhang

Wang

et al. 2020

Energies

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The problem of timely detecting the engine faults that make engine operating parameters exceed their control limits has been well-solved. However, in practice, a fault of a diesel engine can be present with weak signatures, with the parameters fluctuating within their control limits when the fault occurs. The weak signatures of engine faults bring considerable difficulties to the effective condition monitoring of diesel engines. In this paper, a multivariate statistics-based fault detection approach is proposed to monitor engine faults with weak signatures by taking the correlation of various parameters into consideration. This approach firstly uses principal component analysis (PCA) to project the engine observations into a principal component subspace (PCS) and a residual subspace (RS). Two statistics, i.e., Hotelling’s T 2 and Q statistics, are then introduced to detect deviations in the PCS and the RS, respectively. The Hotelling’s T 2 and Q statistics are constructed by taking the correlation of various parameters into consideration, so that faults with weak signatures can be effectively detected via these two statistics. In order to reasonably determine the control limits of the statistics, adaptive kernel density estimation (KDE) is utilized to estimate the probability density functions (PDFs) of Hotelling’s T 2 and Q statistics. The control limits are accordingly derived from the PDFs by giving a desired confidence level. The proposed approach is demonstrated by using a marine diesel engine. Experimental results show that the proposed approach can effectively detect engine faults with weak signatures.

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Monitoring of fuel oil process of marine diesel engine

Cited by 19 publications

References 16 publications

A Technological Understanding of Biofilm Detection Techniques: A Review

A Technological Understanding of Biofilm Detection Techniques: A Review

Assorted control charts: An efficient statistical approach to monitor pH values in ecotoxicology lab

A Multivariate Statistics-Based Approach for Detecting Diesel Engine Faults with Weak Signatures

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