Model parameter estimation and residual life span prediction of pneumatic diaphragm pump based on hidden Markov model in intelligent spraying

Abstract

Pneumatic diaphragm pump is an important part in intelligent spraying. When pneumatic diaphragm pump does not work normally, the entire intelligent spraying product line will be malfunctioned. To maintain and manage pneumatic diaphragm pump effectively, the grade analysis of the health status of pneumatic diaphragm pump is generally used according to its working state. Due to the effects of condition monitoring and random faults, some observable health predictions are often inaccurate. There are very few papers dealing with the health monitoring of pneumatic diaphragm pump and their estimation of residual life span. In this article, a method with vector autoregressive model and continuous-time hidden Markov model was proposed to analyze and evaluate the life span of pneumatic diaphragm pump based on the estimation error of the health condition and the cumulative deterioration of pneumatic diaphragm pump. It is modeled through a continuous-time Markov chain with three states, which includes unobservable healthy state 0, unobservable warning state 1, and observable fault state 2. The expectation–maximization algorithm is used to estimate the model parameters of the fitted hidden Markov. Through the posterior probability of pneumatic diaphragm pump in warning state 1, the derived conditional reliability function and mean residual life span formula can be calculated to evaluate the residual life span of pneumatic diaphragm pump. The results showed that the method can effectively predict the residual life span of pneumatic diaphragm pump, illustrate the effectiveness of the model, and improve the accuracy of the health status rating.

Multistate Diagnosis and Prognosis of Lubricating Oil Degradation Using Sticky Hierarchical Dirichlet Process–Hidden Markov Model Framework

“…The hidden Markov model (HMM) has been applied to forecast health status based on observations in Refs. [10][11][12][13]. HMM has also been a vital modeling tool in the field of bioinformatics [14], speech [15], image [16], and handwriting recognition [17], as well as financial time series predictions [18].…”

“…The new state, Z 0 = 0, depicts the new oil and initial state of degradation. The chain rule then gives the state sequence probability [10] P(…”

“…The weak limit parameter, L, i.e., the state truncation level, is set to 20. The hyperparameter search range is set to (1,10), (1,10), and (1,50) for α, β, and γ, respectively, with 300 iterations, and the log-likelihood convergence is shown in Figure 5.…”

A Novel Fault Diagnosis Strategy for Diaphragm Pumps Based on Signal Demodulation and PCA-ResNet

“…Liu et al [ 8 ] proposed an efficient fault diagnosis method based on continuous wavelet transform and least squares method for valve spring fracture faults in pumps, accurately identifying singular points of fault signals and determining spring fracture faults (operating data method). Yan et al [ 9 ] applied the cumulative degradation estimation error method to the fault diagnosis of diaphragm pumps and proposed a diaphragm pump operation evaluation method based on the Markov model and vector autoregressive model (artificial intelligence method). The accuracy of the diaphragm pump health status evaluation was improved.…”