Stochastic Modelling for Condition Based Maintenance

This Master's thesis covers almost all aspects of Condition Based Maintenance (CBM). All objectives in Chapter 1 are met. The thesis is mainly comprised of three parts. First part introduces the world of CBM to readers. This part presents data acquisition, data processing and databases, which are the foundation to CBM. Then it highlights models which are divided into physics based models, data-driven models and hybrid models, for diagnostic and prognostic use. Three promising diagnostic and prognostic models are specified, i.e., Markov model, Artificial neural networks and the time-dependent proportional hazard model. Afterwards, CBM main steps are presented in figure 2.2. This figure is made based on a large quantity of literature review and can function as an index when readers are querying CBM data, diagnostic and prognostic models and steps. It can also give readers a whole picture of CBM. Next, introductions of Prognostic and Health Management (PHM), CBM industry applications and CBM state of the art are followed. Specific challenges, phenomena and questions are summarized. Second part presents a Matlab toolbox made by the writer. This toolbox estimates components' Remaining Useful Life (RUL) with a standard deviation, the probability to survive till the next maintenance time and Probability of Failure on Demand (PFD) based on numerous simulations. The stochastic processes behind are the (continuous time) Markov model, the Brownian motion process and the Gamma process. Users can choose among them in the toolbox. Specially, MTTF and the steady-state-probability can be achieved by using the Markov model. This toolbox is used often in the next part when data is analyzed. The writer makes the graphic interface of this toolbox easy for people to use. All instructions are given. All code is also attached, from the whole toolbox code to a tiny simulation step with detailed explanations. This toolbox makes it possible for the people with little knowledge in statistics and maintenance to make their own maintenance plans. This toolbox can be download on-line. The third part of this Master's thesis uses 7 statistic models and 3 stochastic processes to model the degradation process of the elastomeric annular body from the annular preventer of a BOP system. To make these models, many relevant papers and books are studied. In this Master's thesis, these models are not just "theories" or "formulas". Instead, for each model, the writer gives a vivid example by analyzing the data with all Matlab code and detailed explanations following. The writer believe by doing this readers can have a deeper understanding of each model. They may use one of these models for their own data in the future. To make it easier for readers to follow, the difficulty of these models is increasing one after one. The complex model can give a more precise estimation of the lifetime with more influence factors being taken into consideration. As to the structure of the third part, firstly, much literature about BOP is read. A brief description about BOP systems is followed. Virtual failure data is simulated based on a trustful BOP reliability report. The exponential model is firstly used to give a preliminary understanding of the data. Afterwards, the Weibull model, the log-logistic model and the log-normal model are used. All these models use Maximum likelihood Estimation (MLE). Minitab is the analyzing software used here. Then, the Brownian motion process is introduced to model the degradation process. Next, the covariates are introduced (e.g., the temperature). The Weibull regression model is elaborated followed by Proportional hazard model (Cox regression model) and Arrhenius model. These three are very promising models used in CBM. Brownian motion is used again to model the degradation. However, this time, the covariates are taken into account. It leads to the change of the path of the Brownian motion process each time when covariates are changing. It is more complex but more realistic. This is the final step to model the degradation in a component level. To model the degradation in a system level, two extra models are included. That is the Markov model and the Brownian motion process for a koon system with covariates. They are shown in the same chapter. Finally, relevant maintenance plans are made based on the result of "RUL" and "the probability to survive till the next maintenance interval

Stochastic Modelling for Condition Based Maintenance

This Master's thesis covers almost all aspects of Condition Based Maintenance (CBM). All objectives in Chapter 1 are met. The thesis is mainly comprised of three parts. First part introduces the world of CBM to readers. This part presents data acquisition, data processing and databases, which are the foundation to CBM. Then it highlights models which are divided into physics based models, data-driven models and hybrid models, for diagnostic and prognostic use. Three promising diagnostic and prognostic models are specified, i.e., Markov model, Artificial neural networks and the time-dependent proportional hazard model. Afterwards, CBM main steps are presented in figure 2.2. This figure is made based on a large quantity of literature review and can function as an index when readers are querying CBM data, diagnostic and prognostic models and steps. It can also give readers a whole picture of CBM. Next, introductions of Prognostic and Health Management (PHM), CBM industry applications and CBM state of the art are followed. Specific challenges, phenomena and questions are summarized. Second part presents a Matlab toolbox made by the writer. This toolbox estimates components' Remaining Useful Life (RUL) with a standard deviation, the probability to survive till the next maintenance time and Probability of Failure on Demand (PFD) based on numerous simulations. The stochastic processes behind are the (continuous time) Markov model, the Brownian motion process and the Gamma process. Users can choose among them in the toolbox. Specially, MTTF and the steady-state-probability can be achieved by using the Markov model. This toolbox is used often in the next part when data is analyzed. The writer makes the graphic interface of this toolbox easy for people to use. All instructions are given. All code is also attached, from the whole toolbox code to a tiny simulation step with detailed explanations. This toolbox makes it possible for the people with little knowledge in statistics and maintenance to make their own maintenance plans. This toolbox can be download on-line. The third part of this Master's thesis uses 7 statistic models and 3 stochastic processes to model the degradation process of the elastomeric annular body from the annular preventer of a BOP system. To make these models, many relevant papers and books are studied. In this Master's thesis, these models are not just "theories" or "formulas". Instead, for each model, the writer gives a vivid example by analyzing the data with all Matlab code and detailed explanations following. The writer believe by doing this readers can have a deeper understanding of each model. They may use one of these models for their own data in the future. To make it easier for readers to follow, the difficulty of these models is increasing one after one. The complex model can give a more precise estimation of the lifetime with more influence factors being taken into consideration. As to the structure of the third part, firstly, much literature about BOP is read. A brief description about BOP systems is followed. Virtual failure data is simulated based on a trustful BOP reliability report. The exponential model is firstly used to give a preliminary understanding of the data. Afterwards, the Weibull model, the log-logistic model and the log-normal model are used. All these models use Maximum likelihood Estimation (MLE). Minitab is the analyzing software used here. Then, the Brownian motion process is introduced to model the degradation process. Next, the covariates are introduced (e.g., the temperature). The Weibull regression model is elaborated followed by Proportional hazard model (Cox regression model) and Arrhenius model. These three are very promising models used in CBM. Brownian motion is used again to model the degradation. However, this time, the covariates are taken into account. It leads to the change of the path of the Brownian motion process each time when covariates are changing. It is more complex but more realistic. This is the final step to model the degradation in a component level. To model the degradation in a system level, two extra models are included. That is the Markov model and the Brownian motion process for a koon system with covariates. They are shown in the same chapter. Finally, relevant maintenance plans are made based on the result of "RUL" and "the probability to survive till the next maintenance interval

Structural basis for flg22-induced activation of the Arabidopsis FLS2-BAK1 immune complex

Flagellin perception in Arabidopsis is through recognition of its highly conserved N-terminal epitope (flg22) by flagellin-sensitive 2 (FLS2). Flg22 binding induces FLS2 heteromerization with BRASSINOSTEROID INSENSITIVE 1-associated kinase 1 (BAK1) and their reciprocal activation followed by plant immunity. Here, we report the crystal structure of FLS2 and BAK1 ectodomains complexed with flg22 at 3.06 angstroms. A conserved and a nonconserved site from the inner surface of the FLS2 solenoid recognize the C- and N-terminal segment of flg22, respectively, without oligomerization or conformational changes in the FLS2 ectodomain. Besides directly interacting with FLS2, BAK1 acts as a co-receptor by recognizing the C terminus of the FLS2-bound flg22. Our data reveal the molecular mechanisms underlying FLS2-BAK1 complex recognition of flg22 and provide insight into the immune receptor complex activation

Downregulation of Akt1 Inhibits Anchorage-Independent Cell Growth and Induces Apoptosis in Cancer Cells

The serine/threonine kinases, Akt1/PKBα, Akt2/PKBβ, and Akt3/PKBγ, play a critical role in preventing cancer cells from undergoing apoptosis. However, the function of individual Akt isoforms in the tumorigenicity of cancer cells is still not well defined. In the current study, we used an Akt1 antisense oligonucleotide (AS) to specifically downregulate Akt1 protein in both cancer and normal cells. Our data indicate that Akt1 AS treatment inhibits the ability of MiaPaCa-2, H460, HCT-15, and HT1080 cells to grow in soft agar. The treatment also induces apoptosis in these cancer cells as demonstrated by FACS analysis and a caspase activity assay. Conversely, Akt1 AS treatment has little effect on the cell growth and survival of normal human cells including normal human fibroblast (NHF), fibroblast from muscle (FBM), and mammary gland epithelial 184B5 cells. In addition, Akt1 AS specifically sensitizes cancer cells to typical chemotherapeutic agents. Thus, Akt1 is indispensable for maintaining the tumorigenicity of cancer cells. Inhibition of Akt1 may provide a powerful sensitization agent for chemotherapy specifically in cancer cells

Structural basis for specific flagellin recognition by the NLR protein NAIP5

The nucleotide-binding domain- and leucine-rich repeat (LRR)-containing proteins (NLRs) function as intracellular immune receptors to detect the presence of pathogen- or host-derived signals. The mechanisms of how NLRs sense their ligands remain elusive. Here we report the structure of a bacterial flagellin derivative in complex with the NLR proteins NAIP5 and NLRC4 determined by cryo-electron microscopy at 4.28 Å resolution. The structure revealed that the flagellin derivative forms two parallel helices interacting with multiple domains including BIR1 and LRR of NAIP5. Binding to NAIP5 results in a nearly complete burial of the flagellin derivative, thus stabilizing the active conformation of NAIP5. The extreme C-terminal side of the flagellin is anchored to a sterically constrained binding pocket of NAIP5, which likely acts as a structural determinant for discrimination of different bacterial flagellins by NAIP5, a notion further supported by biochemical data. Taken together, our results shed light on the molecular mechanisms underlying NLR ligand perception

Structural basis for specific flagellin recognition by the NLR protein NAIP5

full vie

Parkin promotes proteasomal degradation of p62: implication of selective vulnerability of neuronal cells in the pathogenesis of Parkinson's disease

Mutations or inactivation of parkin, an E3 ubiquitin ligase, are associated with familial form or sporadic Parkinson's disease (PD), respectively, which manifested with the selective vulnerability of neuronal cells in substantia nigra (SN) and striatum (STR) regions. However, the underlying molecular mechanism linking parkin with the etiology of PD remains elusive. Here we report that p62, a critical regulator for protein quality control, inclusion body formation, selective autophagy and diverse signaling pathways, is a new substrate of parkin. P62 levels were increased in the SN and STR regions, but not in other brain regions in parkin knockout mice. Parkin directly interacts with and ubiquitinates p62 at the K13 to promote proteasomal degradation of p62 even in the absence of ATG5. Pathogenic mutations, knockdown of parkin or mutation of p62 at K13 prevented the degradation of p62. We further showed that parkin deficiency mice have pronounced loss of tyrosine hydroxylase positive neurons and have worse performance in motor test when treated with 6-hydroxydopamine hydrochloride in aged mice. These results suggest that, in addition to their critical role in regulating autophagy, p62 are subjected to parkin mediated proteasomal degradation and implicate that the dysregulation of parkin/p62 axis may involve in the selective vulnerability of neuronal cells during the onset of PD pathogenesis

Implantation of hydrogel-liposome nanoplatform inhibits glioblastoma relapse by inducing ferroptosis

Glioblastoma is acknowledged as the most aggressive cerebral tumor in adults. However, the efficacy of current standard therapy is seriously undermined by drug resistance and suppressive immune microenvironment. Ferroptosis is a recently discovered form of iron-dependent cell death that may have excellent prospect as chemosensitizer. The utilization of ferropotosis inducer Erastin could significantly mediate chemotherapy sensitization of Temozolomide and exert anti-tumor effects in glioblastoma. In this study, a combination of hydrogel-liposome nanoplatform encapsulated with Temozolomide and ferroptosis inducer Erastin was constructed. The αvβ3 integrin-binding peptide cyclic RGD was utilized to modify codelivery system to achieve glioblastoma targeting strategy. As biocompatible drug reservoirs, cross-linked GelMA (gelatin methacrylamide) hydrogel and cRGD-coated liposome realized the sustained release of internal contents. In the modified intracranial tumor resection model, GelMA-liposome system achieved slow release of Temozolomide and Erastin in situ for more than 14 d. The results indicated that nanoplatform (T+E@LPs-cRGD+GelMA) improved glioblastoma sensitivity to chemotherapeutic temozolomide and exerted satisfactory anti-tumor effects. It was demonstrated that the induction of ferroptosis could be utilized as a therapeutic strategy to overcome drug resistance. Furthermore, transcriptome sequencing was conducted to reveal the underlying mechanism that the nanoplatform (T+E@LPs-cRGD+GelMA) implicated in. It is suggested that GelMA-liposome system participated in the immune response and immunomodulation of glioblastoma via interferon/PD-L1 pathway. Collectively, this study proposed a potential combinatory therapeutic strategy for glioblastoma treatment