Fault diagnosis method for water quality monitoring and control equipment in aquaculture based on multiple SVM combined with D-S evidence theory

Yang, Hao; Hassan, Shahbaz Gul; Wang, Liang; Li, Bingbing

doi:10.1016/j.compag.2017.05.016

Cited by 25 publications

(8 citation statements)

References 39 publications

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“…This work studied the feasibility of a new water quality sensor for RAS, based on the record and prediction of toxicity by us of linear statistical models and a whole-cell biosensor. Over the years, the industry and academia have been focused on the use of wireless sensor nodes employing electronic and electrochemical sensors [ 31 , 32 , 33 ]. These systems focus not only in monitoring the water quality of the RAS system, but also provide control of the system during normal operation through environmental sensors and feed and also provide an alarm, if needed.…”

Section: Resultsmentioning

confidence: 99%

Monitoring Aquaculture Water Quality: Design of an Early Warning Sensor with Aliivibrio fischeri and Predictive Models

Silva

Yang

Pires

et al. 2018

Sensors

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A novel toxicity-warning sensor for water quality monitoring in recirculating aquaculture systems (RAS) is presented. The design of the sensor system mainly comprises a whole-cell biosensor. Aliivibrio fischeri, a luminescent bacterium widely used in toxicity analysis, was tested for a mixture of known fish-health stressors, namely nitrite, un-ionized ammonia, copper, aluminum and zinc. Two toxicity predictive models were constructed. Correlation, root mean squared error, relative error and toxic behavior were analyzed. The linear concentration addition (LCA) model was found suitable to ally with a machine learning algorithm for prediction of toxic events, thanks to additive behavior near the limit concentrations for these stressors, with a root-mean-squared error (RMSE) of 0.0623, and a mean absolute error of 4%. The model was proved to have a smaller relative deviation than other methods described in the literature. Moreover, the design of a novel microfluidic chip for toxicity testing is also proposed, which is to be integrated in a fluidic system that functions as a bypass of the RAS tank to enable near-real time monitoring. This chip was tested with simulated samples of RAS water spiked with zinc, with an EC50 of 6,46E-7 M. Future work will be extended to the analysis of other stressors with the novel chip.

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Section: Resultsmentioning

confidence: 99%

Monitoring Aquaculture Water Quality: Design of an Early Warning Sensor with Aliivibrio fischeri and Predictive Models

Silva

Yang

Pires

et al. 2018

Sensors

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show abstract

“…The diagnosis information based on mathematical models can be obtained from the working condition data, so that the faults can be identified and located without powering off and disassembly for inspection. In view of the complexity of multivariate combination feature extraction, strong autocorrelation of variables and significant nonstationarity of fault conditions, a fault feature extraction method based on DPCA-VMD-SVD is proposed (Yang et al 2017 ). Based on the dynamic principal component analysis (DPCA), the basic matrix of the sample is mapped into the dynamic principal component space, and 31 dynamic principal components are selected according to the proportion of cumulative variance.…”

Section: Data-driven Decision-makingmentioning

confidence: 99%

Intelligent fish farm—the future of aquaculture

Wang

et al. 2021

Aquacult Int

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With the continuous expansion of aquaculture scale and density, contemporary aquaculture methods have been forced to overproduce resulting in the accelerated imbalance rate of water environment, the frequent occurrence of fish diseases, and the decline of aquatic product quality. Moreover, due to the fact that the average age profile of agricultural workers in many parts of the world are on the higher side, fishery production will face the dilemma of shortage of labor, and aquaculture methods are in urgent need of change. Modern information technology has gradually penetrated into various fields of agriculture, and the concept of intelligent fish farm has also begun to take shape. The intelligent fish farm tries to deal with the precise work of increasing oxygen, optimizing feeding, reducing disease incidences, and accurately harvesting through the idea of “replacing human with machine,” so as to liberate the manpower completely and realize the green and sustainable aquaculture. This paper reviews the application of fishery intelligent equipment, IoT, edge computing, 5G, and artificial intelligence algorithms in modern aquaculture, and analyzes the existing problems and future development prospects. Meanwhile, based on different business requirements, the design frameworks for key functional modules in the construction of intelligent fish farm are proposed.

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“…However, it is almost impossible to obtain enough samples for a maintainability demonstration during operational tests and evaluations because the tests are expensive. Generally, the problem of insufficient samples can be dealt with by using probabilistic and statistical approaches, such as Bayesian techniques [3][4][5][6], bootstrap methods [7,8], and Dempster-Shafer (D-S) evidence theory [9][10][11]. Of these, Bayesian methods have increasingly become the de facto option in reliability and maintainability engineering [12].…”

Section: Introductionmentioning

confidence: 99%

A Maintenance Task Similarity-Based Prior Elicitation Method for Bayesian Maintainability Demonstration

Jian-ping

2020

Mathematical Problems in Engineering

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Prior distribution elicitation is a challenging problem for a Bayesian inference-based mean time to repair (MTTR) demonstration because if inaccurate prior information is introduced into the prior distribution, the results become unreliable. This paper proposes a novel maintenance task representation model based on the similarity of attributed maintenance items. A novel similarity computation algorithm for maintenance tasks is then formulated on the basis of this model. Optimistic and pessimistic values are ascertained from the time data for similar maintenance tasks to obtain a prior distribution. The main idea is to separate maintenance tasks into distinct items and use attribute sets to extract key features. Each pair of items is then compared to quantify the differences between reference and candidate tasks. Candidate tasks with an acceptable difference from the reference task are taken as prior information sources for constructing the prior distribution. A case study involving a high-frequency (HF) transceiver MTTR Bayesian demonstration shows that the proposed method can effectively obtain maintenance tasks similar to those of information sources for prior distribution elicitation.

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Fault diagnosis method for water quality monitoring and control equipment in aquaculture based on multiple SVM combined with D-S evidence theory

Cited by 25 publications

References 39 publications

Monitoring Aquaculture Water Quality: Design of an Early Warning Sensor with Aliivibrio fischeri and Predictive Models

Monitoring Aquaculture Water Quality: Design of an Early Warning Sensor with Aliivibrio fischeri and Predictive Models

Intelligent fish farm—the future of aquaculture

A Maintenance Task Similarity-Based Prior Elicitation Method for Bayesian Maintainability Demonstration

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