Variation Modeling of Lean Manufacturing Performance Using Fuzzy Logic Based Quantitative Lean Index

The lean index is the sum of weighted scores of performance variables that describe the lean manufacturing characteristics of a system. Various quantitative lean index models have been advanced for assessing lean manufacturing performance. These models are represented by deterministic variables and do not consider variation in manufacturing systems. In this article variation is modeled in a quantitative fuzzy logic based lean index and compared with traditional deterministic modeling. By simulating the lean index model for a manufacturing case it is found that the latter tend to under or overestimate performance and the former provides a more robust lean assessment

A high level e-maintenance architecture to support on-site teams

Emergent architectures and paradigms targeting reconfigurable manufacturing systems increasingly rely on intelligent modules to maximize the robustness and responsiveness of modern installations. Although intelligent behaviour significantly minimizes the occurrence of faults and breakdowns it does not exclude them nor can prevent equipment’s normal wear. Adequate maintenance is fundamental to extend equipments’ life cycle. It is of major importance the ability of each intelligent device to take an active role in maintenance support. Further this paradigm shift towards “embedded intelligence”, supported by cross platform technologies, induces relevant organizational and functional changes on local maintenance teams. On the one hand, the possibility of outsourcing maintenance activities, with the warranty of a timely response, through the use of pervasive networking technologies and, on the other hand, the optimization of local maintenance staff are some examples of how IT is changing the scenario in maintenance. The concept of e-maintenance is, in this context, emerging as a new discipline with defined socio-economic challenges. This paper proposes a high level maintenance architecture supporting maintenance teams’ management and offering contextualized operational support. All the functionalities hosted by the architecture are offered to the remaining system as network services. Any intelligent module, implementing the services’ interface, can report diagnostic, prognostic and maintenance recommendations that enable the core of the platform to decide on the best course of action.manufacturing systems, platform technologies, maintenance

A Voice is Worth a Thousand Words: The Implications of the Micro-Coding of Social Signals in Speech for Trust Research

While self-report measures are often highly reliable for field research on trust (Mayer and Davis, 1999), subjects often cannot complete surveys during real time interactions. In contrast, the social signals that are embedded in the non-linguistic elements of conversations can be captured in real time and extracted with the assistance of computer coding. This chapter seeks to understand how computer-coded social signals are related to interpersonal trust

THEORETICAL AND EMPIRICAL PROBLEMS IN MODELING OPTIMAL REPLACEMENT OF FARM MACHINES

Farm Management,

A convolutional neural network aided physical model improvement for AC solenoid valves diagnosis

This paper focuses on the development of a physics-based diagnostic tool for alternating current (AC) solenoid valves which are categorized as critical components of many machines used in the process industry. Signal processing and machine learning based approaches have been proposed in the literature to diagnose the health state of solenoid valves. However, the approaches do not give a physical explanation of the failure modes. In this work, being capable of diagnosing failure modes while using a physically interpretable model is proposed. Feature attribution methods are applied to CNN on a large data set of the current signals acquired from accelerated life tests of several AC solenoid valves. The results reveal important regions of interest on current signals that guide the modeling of the main missing component of an existing physical model. Two model parameters, which are the shading ring and kinetic coulomb forces, are then identified using current measurements along the lifetime of valves. Consistent trends are found for both parameters allowing to diagnose the failure modes of the solenoid valves. Future work will consist of not only diagnosing the failure modes, but also of predicting the remaining useful life

Presence and rehabilitation: toward second-generation virtual reality applications in neuropsychology

Virtual Reality (VR) offers a blend of attractive attributes for rehabilitation. The most exploited is its ability to create a 3D simulation of reality that can be explored by patients under the supervision of a therapist. In fact, VR can be defined as an advanced communication interface based on interactive 3D visualization, able to collect and integrate different inputs and data sets in a single real-like experience. However, "treatment is not just fixing what is broken; it is nurturing what is best" (Seligman & Csikszentmihalyi). For rehabilitators, this statement supports the growing interest in the influence of positive psychological state on objective health care outcomes. This paper introduces a bio-cultural theory of presence linking the state of optimal experience defined as "flow" to a virtual reality experience. This suggests the possibility of using VR for a new breed of rehabilitative applications focused on a strategy defined as transformation of flow. In this view, VR can be used to trigger a broad empowerment process within the flow experience induced by a high sense of presence. The link between its experiential and simulative capabilities may transform VR into the ultimate rehabilitative device. Nevertheless, further research is required to explore more in depth the link between cognitive processes, motor activities, presence and flow

Developing Leading and Lagging Indicators to Enhance Equipment Reliability in a Lean System

With increasing complexity in equipment, the failure rates are becoming a critical metric due to the unplanned maintenance in a production environment. Unplanned maintenance in manufacturing process is created issues with downtimes and decreasing the reliability of equipment. Failures in equipment have resulted in the loss of revenue to organizations encouraging maintenance practitioners to analyze ways to change unplanned to planned maintenance. Efficient failure prediction models are being developed to learn about the failures in advance. With this information, failures predicted can reduce the downtimes in the system and improve the throughput. The goal of this thesis is to predict failure in centrifugal pumps using various machine learning models like random forest, stochastic gradient boosting, and extreme gradient boosting. For accurate prediction, historical sensor measurements were modified into leading and lagging indicators which explained the failure patterns in the equipment were developed. The best subset of indicators was selected by filtering using random forest and utilized in the developed model. Finally, the models give a probability of failure before the failure occurs. Appropriate evaluation metrics were used to obtain the accurate model. The proposed methodology was illustrated with two case studies: first, to the centrifugal pump asset performance data provided by Meridium, Inc. and second, the data collected from aircraft turbine engine provided in the NASA prognostics data repository. The automated methodology was shown to develop and identify appropriate failure leading and lagging indicators in both cases and facilitate machine learning model development

Can involving clients in simulation studies help them solve their future problems? A transfer of learning experiment

It is often stated that involving the client in operational research studies increases conceptual learning about a system which can then be applied repeatedly to other, similar, systems. Our study provides a novel measurement approach for behavioural OR studies that aim to analyse the impact of modelling in long term problem solving and decision making. In particular, our approach is the first to operationalise the measurement of transfer of learning from modelling using the concepts of close and far transfer, and overconfidence. We investigate learning in discrete-event simulation (DES) projects through an experimental study. Participants were trained to manage queuing problems by varying the degree to which they were involved in building and using a DES model of a hospital emergency department. They were then asked to transfer learning to a set of analogous problems. Findings demonstrate that transfer of learning from a simulation study is difficult, but possible. However, this learning is only accessible when sufficient time is provided for clients to process the structural behaviour of the model. Overconfidence is also an issue when the clients who were involved in model building attempt to transfer their learning without the aid of a new model. Behavioural OR studies that aim to understand learning from modelling can ultimately improve our modelling interactions with clients; helping to ensure the benefits for a longer term; and enabling modelling efforts to become more sustainable