Sewage Treatment Fault Diagnosis And Software Development Based On Weighted Extreme Learning Machine Ensemble Algorithm

Sewage treatment system is a complicated biochemical reaction system with many influencing factors.Sewage treatment plants are prone to operating faults in long-term operation.If these faults are not handled properly in time,it will lead to a series of serious problems.The algorithm of machine learning and data mining can analyze the data of sewage treatment system in real time,accurately and objectively judge the operating status of the sewage treatment process,and diagnose and handle the fault in time,which is of great significance to sewage treatment.An important problem existing in the fault diagnosis of sewage treatment is that the data of the normal state and the fault state are not balanced.However,most of the classical machine learning algorithms can not deal with the imbalanced data and often judge the fault state as a normal state,the sewage treatment plant will cause great economic losses without timely treatment.In this paper,the sewage treatment as the application background,in view of the imbalanced data of the operating status of the sewage treatment process,based on the weighted extreme learning machine and the ensemble algorithm,proposes an integrated algorithm model based on Weighted Extreme Learning Machine(WELM),which is AdaWELM.By combining WELM's sample weight setting and AdaBoost's iterative updating of sample weights,the existing integration algorithm is improved at the algorithm level.In addition,the use of extreme learning machine using a single hidden layer feedforward neural network does not require iterative adjustment of hidden layer node characteristics,which greatly improves the learning speed of the integrated algorithm.AdaWELM makes it easier to categorize data in multiple categories while improving classification efficiency.Applied to the sewage treatment process fault diagnosis,can improve the G-mean value of imbalanced data,fault type recall rate and diagnosis of real-time.Then,by further studying the theory and combination mechanism of AdaBoost and WELM,we propose a new AdaGELM algorithm based on G-mean for weighted learning machine.Compared with AdaWELM,the overall G-mean training goal is to improve the recall rate and overall G-mean value of sewage data fault categories,and the real-time diagnosis is equivalent.Finally,this paper uses MATLAB software to design a fault diagnosis research platform for sewage treatment,integrates the classic classification algorithm model and the two models proposed in this paper,and designs a complete model training platform and online testing platform,which can realize training model parameter preservation,image display,performance comparison,fault identification and fault recovery in sewage treatment fault diagnosis process,which greatly facilitates the developer of the imbalanced data classification and sewage treatment fault diagnosis online monitoring.