Diagnosis of Multiple Fixture Faults in Multiple-Station Manufacturing Processes Based on State Space Approach

Dimensional quality is one of the most critical challenges in industries, which uses the multistage manufacturing process (MMP) such as assembly and machining for automotive and aerospace industries. According to investigations, fixture faults accounted for 72% of all the dimensional faults. Previous studies focused on only one fault or multiple faults occurred in one station or one fault in multiple stations, but these cases rarely appear in the real manufacturing. This paper presents a method for diagnosis of multiple fixture faults in the multi-station manufacturing process. The proposed method is based on the state space model of the MMP processes, which carries the information of the fixture layout geometry and sensor position. To identify the root cause, three continuous steps were used: a) development of the state space model and the construction of the statistics variables on offline mode, b) measurement of the coordinate measuring machines data on online mode and calculation of the statistics variables, and c) diagnostic algorithm for identifying the root cause. The presented paper integrates the state space model of the manufacturing processes and hypothesis test considering the impact of the measure noises. A case study verifies the proposed method.

Evaluating recurrent episodes of malaria incidence in Timika,Indonesia,through a Markovian multiple-state model

Background:The high prevalence of malaria in endemic areas generally stems from recurrence events,characterized by the appearance of malaria symptoms at the time of examination;nearly every resident is at risk of experiencing such a recurrence.The verified presence of Plasmodium sp is referred to as the Confirmed state,while the condition without confirmed P.falciparum is called the Undetected Parasitaemia state.After malaria treatment,a person can be in Aparasitaemic state or return to an Undetected Parasitaemia or Confirmed state due to non-adherence in complying with malaria therapy.In this study,we evaluate the characteristics of malaria recurrence in Timika,Indonesia,using the Markovian multiple-state model.In addition,we also simulate the probability of malaria recurrence after the implementation of several control strategies,including prevention strategies using insecticide-treated nets(ITNs)and indoor residual spraying(IRS).Objective:This study aims to identify the transition probabilities of malaria recurrence with and without control strategies.Methods:We use data from the medical records of malaria patients from the Naena Muktipura sub-health center in Timika,Papua,Indonesia,from March 2020 to March 2021.The data were grouped into two age categories:those under or over 24 years.The incidence of malaria in this area was modeled using a Markovian multiple-state model,dividing the incidence data based on the character of the patient's condition(Undetected Parasitaemia,Confirmed,or Aparasitaemic states)in order to obtain the patient's transition probabilities in each state.Furthermore,we simulate the recurrence probability given specific control strategies.Results:There were 964 visits to the sub-health center at Naena Muktipura in which symptoms of malaria were reported.Specifically,the number of the malaria incidences in the groups under and over age 24 were 456 and 508,respectively.The modeling results indicate that the probability of recurrence in the over-24 age group is generally higher than that in the under-24 age group.However,the probability of this recurrence decreases over time.Furthermore,providing a control strategy can reduce the probability of recurrence and increase the probability of recovery for these patients.Conclusion:In endemic areas,adherence to treatment and preventive measures can accelerate the healing process and reduce the probability of malaria recurrence.With proper treatment management,the use of ITNs and the application of IRS,the incidence of malaria can be reduced and recovery can be accelerated.

Universal generating function based probabilistic production simulation for wind power integrated power systems

According to the demand of sustainable development and low-carbon electricity, it is important to develop clean resources and optimize scheduling generation mix. Firstly, a novel method for probabilistic production simulation for wind power integrated power systems is proposed based on universal generating function(UGF), which completes the production simulation with the chronological wind power and load demand. Secondly,multiple-period multiple-state wind power model and multiple-state thermal unit power model are adopted, and both thermal power and wind power are coordinately scheduled by the comprehensive cost including economic cost and environmental cost. Furthermore, the accommodation and curtailment of wind power is synergistically considered according to the available regulation capability of conventional generators in operation. Finally, the proposed method is verified and compared with conventional convolution method in the improved IEEE-RTS 79 system.