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In this article, the authors focus on the introduction of a hybrid method for risk-based fault detection (FD) using dynamic principal component analysis (DPCA) and failure method and effect analysis (FMEA) based Bayesian networks (BNs). The FD problem has garnered great interest in industrial application, yet methods for integrating process risk into the detection procedure are still scarce. It is, however, critical to assess the risk each possible process fault holds to differentiate between non-safety-critical and safety-critical abnormalities and thus minimize alarm rates. The proposed method utilizes a BN established through FMEA analysis of the supervised process and the results of dynamical principal component analysis to estimate a modified risk priority number (RPN) of different process states. The RPN is used parallel to the FD procedure, incorporating the results of both to differentiate between process abnormalities and highlight critical issues. The method is showcased using an industrial benchmark problem as well as the model of a reactor utilized in the emerging liquid organic hydrogen carrier (LOHC) technology.
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OBJECTIVE: The majority of cranial defects are results of surgical intervention. The defect must be covered within resonable period of time usually after 4-6 week given the fact that the replacement of bone improve the brain circulation. Number of surgical techniques and materials are available to perform cranioplasty. Due to favorable properties we chosed ultra high molecular weight polyethylene as material. In this paper the authors show a procedure which allows tailored artificial bone replacement using state of art medical and engineering techniques. METHODS: between 2004 and 2012, 19 patients were operated on cranial bone defect and a total of 22 3D custom-designed implants were implanted. The average age of patients was 35.4 years. In 12 patients we performed primary cranioplasty, while seven patients had the replacement at least once. Later the implants had to be removed due to infection or other causes (bone necrosis, fracture). All patients had native and bone-windowed 1 mm resolution CT. The 3D design was made using the original CT images and with design program. Computer controlled lathe was used to prepare a precise-fitting model. During surgery, the defect was exposed and the implant was fixed to normal bone using mini titanium plates and screws. All of our patients had control CT at 3, 6 and 12 months after surgery and at the same time neurological examination. RESULTS: Twenty-one polyethylene and one titanium implants were inserted. The average follow-up of the patients was 21.5 months, ranged from two to 96 months. We follow 12 patients (63.15%) more than one year. No intraoperative implant modifications had to be made. Each of the 22 implant exactly matched the bone defect proved by CT scan. No one of our patients reported aesthetic problems and we did not notice any kind of aesthetic complication. We had short term complication in three cases due to cranioplasty, subdural, epidural haemorrhage and skin defect. CONCLUSION: Polyethylene is in all respects suitable for primary and secondary cranioplasty. Combined with 3D CAD- CAM method excellent aesthetic and functional result was achieved. In our study no case of infection occured. Proper preoperative preparation is important.