In this issue 7 papers included are from different areas of performability engineering. The first paper is on prognostics in which the authors indicate how to develop accurate and applicable data driven models for tool wear estimation and remaining useful life prediction of high speed Computer Numerical Control (CNC) milling machine cutters. The second paper from University of Maryland reviews how system multi-sensor data can be subjected to Bayesian inference to update the understanding of sub-system and component reliability parameters. It also reviews a methodology on how sensor placement can be optimized with multiple objectives, including the utility of inferred reliability information. In the third paper, mathematical models of the various components of the boiler-turbine-generation system are developed and a comprehensive virtual model of a steam turbine power generation unit is presented, which can be used to evaluate parameter values at different stages of the plant, and to determine optimized plant parameters and to design controllers for thermal systems. In the fourth paper, the authors have proposed a novel storage location assignment policy for storing items in a warehouse that corresponds to print production environment. The fifth paper uses hierarchical models prioritized as per the users’ requirement to rank the various plans of Cloud Service Providers considering parameters like Agility, Finance, Performance, Security and Usability. These parameters provide a standardized method for measuring and comparing Infrastructure services of cloud providers. A unified trust evaluation framework described helps customers in selecting a most trustworthy cloud provider. The focus of sixth paper related to sustainability is on the evaluation of ATSWM technologies and their potential for enabling State of Florida USA to reach its recycling goal by 2020. This study can also be generalized to other geographical locations that are experiencing problems in, and seeking ways to achieve sustainable Solid waste management. In the last paper, logistic and simple logistic functions are used as possible classifiers for detection of misfire in IC engines and their performance compared. It has been found that logistic function has better classification accuracy than simple logistic and thus can be used in misfire detection.

The first short communication presents a method of reliability modeling for a two-stage degraded system, using Cumulative damage model which helps in decision-making process on the system maintenance. The second short communication, state probabilities of source nodes and relay nodes in WSNs are evaluated based on a data flow. Results indicate that the energy consumed during operation affects the node reliability in WSNs, and the node state probabilities are related to the distribution of the number of the detecting events.

Effectiveness of tool condition monitoring strategy depends on accuracy in failure prediction (prognostics) of cutting tools. Data driven approaches are generally used for prognostics of cutting tools. Various prognostics models have been proposed in the literature. Performance of these models in terms of accuracy and applicability are found to be the major constraints for use in real industrial applications. Moreover, application of these models is mainly limited to wear prediction. Extension of such models for remaining life prediction is not explored adequately in the literature. The main contribution of this paper is the development of accurate and applicable data driven models for tool wear estimation and remaining useful life prediction of high speed Computer Numerical Control (CNC) milling machine cutters. These models are developed and validated based on experimental data. Proposed models have demonstrated better results in terms of predicting cutter wear as compared to those mentioned in the literature. It also helps in predicting remaining useful life of cutters under following two industrial cases:
- Case I: When only online monitoring data are available.
- Case II: When incidental (or planned) offline inspection data are also available.

Systems, complex or otherwise, can be monitored through sensors placed at various functional levels to infer information about system reliability parameters (and by extension, reliability characteristics). Sensor placement directly affects the quantity and utility of inferred information, and need to be judiciously located throughout the relevant system. Sensors can be embedded or attached upon components, sub-systems or the entire system itself. Functional sensors can detect levels of functionality (including levels of degraded performance) and time to failure of the elements of the system they are monitoring. Data gathered from multiple system sensors will be ‘overlapping’ in that they are drawn from the same process or system at the same time. Overlapping data requires specific consideration for subsequent inference – system states observed by all sensors contextualize the data of all others. This paper is a review of how overlapping sensor data is analyzed in a Bayesian framework, and form part of a sensor placement optimization process to maximize information. This is particularly useful in scenarios where sensors are expensive to install with various resource constraints (such as volume and weight) limiting their use. The paper also presents review of a method of measuring the information utility of various sensor placement arrangements in a Bayesian construct of both on-demand and time-based continuous systems. Prior information is used to simulate evidence sets, which are then used to simulate posterior distribution of reliability metrics of interest. Information utility is derived from these posterior distributions, and an expected information utility is then attributed to sensor placement. Finally, examples of applying the methodologies discussed will be presented.

In boiler turbine power generation units, generation of power as per the demand is an important objective. This requires an effort towards modeling of a power generation system. In this paper, the mathematical models of the various components of the boiler turbine generation unit are developed and a comprehensive virtual model of a steam turbine power generation unit is synthesized. This model is based upon nonlinear mathematical models of the various sections of the plant which are in turn based on the energy balance, thermodynamic principles and semi-empirical equations. The proposed models can be used for various objectives such as to evaluate parameter values at different stages of the plant, to determine optimized plant parameters and to design controllers for thermal systems.

The storage location assignment policy is one of the essential factors of the warehouse order picking. In this paper, we have proposed a novel storage location assignment policy for storing items in a warehouse that corresponds to print production environment. The approach utilizes the concepts of autonomous cell way of partitioning resources in print production systems. The results of the application of this approach are compared with the well-known cube per order index policy (COI) using case studies from print service industry.

Cloud Computing has emerged as a paradigm to deliver on demand resources such as infrastructure and applications to customers as per their requirements on a subscription basis. Due to an exponential increase in the number of service providers, customers need a basis to make a judicious choice of a cloud offering. Thus to assist customers in selecting a most trustworthy cloud provider, a unified trust evaluation framework is needed. In this paper, a hierarchical trust model has been proposed to rate the service providers and their various plans for infrastructure as a service. Such a trust ranking mechanism will help the consumers to compare and rank alternative service providers based on their requirements.

The necessity of establishing sustainable solid waste management (SWM) systems has become apparent in the light of detrimental effects of existing SWM systems on the environment. As a result of the growing pressure for environmental protection and sustainability, the State of Florida has established an ambitious 75% recycling goal, to be achieved by the year 2020. Advanced thermal solid waste management (ATSWM) technologies can push Florida closer to this goal by drastically reducing the amount of waste sent to landfills. However, a comprehensive top-to-bottom assessment of technologies as well as their comparison against one another is crucial prior to the consideration of their implementation. The goal of this study was to evaluate emerging ATSWM technologies for Florida using the Analytic Hierarchy Process (AHP). The results indicated that gasification was ranked the highest while pyrolysis ranked the lowest among the three alternatives examined. However, the weights of criteria used for evaluating the technologies varied among the groups of counties. The implications of the findings for sustainability are also discussed.

Misfire in an Internal Combustion engine is a serious problem that needs to be addressed to prevent engine power loss, fuel wastage and emissions. The vibration signal contains the vibration signature due to misfire and a combination of all vibration emissions of various engine components. The vibration signals acquired from the engine block are used here. Descriptive statistical features are used to represent the useful information stored in vibration signals. Out of all the statistical features, useful features were identified using the J48 decision tree algorithm and then the selected features were classified using logistic and simple logistic functions. In this paper, performance analysis of logistic and simple logistic function has presented for detecting misfire in Spark Ignition (SI) Engine.

This paper aims to develop a method of reliability modeling for a two-stage degraded system, facilitating the decision-making on the system maintenance. Cumulative damage model is used to present the degradation process with changing degradation rates. In the cumulative damage model, shock damage value and shock counting are assumed to be Gamma distribution and homogeneous Poisson process, respectively. Reliability and mean remaining useful life are modeled and analyzed. A numerical example is given to show the performance of the proposed models.

In linear wireless sensor networks (WSNs), when the available energy of a sensor node reduces to below a threshold level, the node will lose its function. The sensor energy consumed can be divided into energy of sensing event, energy of transmitting packets, and energy of receiving packets. In this paper, the node residual energy of a data flow in linear WSNs from source nodes and relay nodes is first evaluated. Four node states are identified based on status of the data packets transmitting. From the viewpoint of reliability theory, a data-flow model is then analyzed to compute state probabilities of source node and relay node in a particular time period. Study results illustrate how the sensor energy affects the node reliability and state probabilities in general, providing guidance for improving the design and operation of WSNs.