ABSTRACT
To detect substation faults for timely repair, this paper proposes a fault detection method that is based on the time series model and the statistical process control method to analyze the regulation and characteristics of the behavior in the switching process. As the first time, this paper proposes a fault detection model using SARIMA, statistical process control (SPC) methods, and 3σ criterion to analyze the characteristics in substation's switching process. The employed approaches are both very common tools in the statistics field, however, via effectively combining them with industrial process fault diagnosis, these common statistical tolls play excellent role to achieve rich technical contributions. Finally, for different fault samples, the proposed method improves the rate of detection by at least 9% (and up to 15%) than other methods.
ABSTRACT
BACKGROUND: Acute kidney injury following percutaneous coronary intervention (PCI) is associated with a worse outcome. However, the risk factors and outcomes of acute kidney injury (AKI) in patients after intracoronary stent implantation are still unknown. METHODS: A retrospective case control study was done in 325 patients who underwent intracoronary stent implantation from January 2010 to March 2011 at the Drum Tower Hospital of Nanjing University School of Medicine. Those were excluded from the study if they had incomplete clinical data. The patients were divided into a normal group and a AKI group according to the standard of post-operation day 7 to identify AKI. The parameters of the patients included: 1) pre-operative ones: age, gender, hypertension, diabetes mellitus, cerebrovascular disease, left ventricular insufficiency, peripheral angiopathy, creatinine, urea nitrogen, estimated glomerular filtration rate (eGFR), hyperuricemia, proteinuria, emergency operation, hydration, medications (ACEI/ARBs, statins); 2) intraoperative ones: dose of contrast media, operative time, hypotension; and 3) postoperative one: hypotension. The parameters were analyzed with univariate analysis and multivariate logistical regression analysis. RESULTS: Of the 325 patients, 51(15.7%) developed AKI. Hospital day and in-hospital mortality were increased significantly in the AKI-group. Univariate analysis showed that age, pre-operative parameters (left ventricular insufficiency, peripheral angiopathy, creatinine, urea nitrogen, estimated glomerular filtration rate, hyperuricemia, proteinuria, hydration), emergency operation, intraoperative parameters (operative time, hypotension) and postoperative hypotension were significantly different. However, multivariate logistic regression analysis revealed that increased age (OR=0.253, 95%CI=0.088-0.727), pre-operative proteinuria (OR=5.351, 95%CI=2.128-13.459), pre-operative left ventricular insufficiency (OR=8.704, 95%CI=3.170-23.898), eGFR≤60 ml/min/1.73 m(2) (OR=6.677, 95%CI=1.167-38.193), prolonged operative time, intraoperative hypotension (OR=25.245, 95%CI=1.001-1.034) were independent risk factors of AKI. CONCLUSIONS: AKI is a common complication and associated with ominous outcome following intracoronary stent implantation. Increased age, pre-operative proteinuria, pre-operative left ventricular insufficiency, pre-operative low estimated glomerular filtration rate, prolonged operative time, intraoperative hypotension were the significant risk factors of AKI.
