Insertion of sustainability concepts in the maintenance strategies to achieve sustainable manufacturing

Companies adopt sustainable practices in order to improve the economic, environmental and social performance of their operations. This fact does not occur with the same intensity in maintenance operations. By adopting sustainable practices during the implementation of maintenance strategies, there will be mitigation of industrial maintenance impacts on sustainability. However, there are few studies on the integration of sustainability in maintenance activities and few companies adopt sustainable maintenance due to lack of knowledge of the subject and its benefits. This paper aims to show how the concepts of sustainability are being inserted in the maintenance strategies. For this purpose, a literature review and a systematic literature review were performed. It was verified that the concepts of sustainability are integrated into maintenance strategies by means of sustainable criteria, with emphasis on lost production cost, spare parts cost and expenditures associated with energy consumption and greenhouse gas emissions (economic dimension), on pollutant emission due to energy consumption during machining/manufacturing (environmental dimension) and on health and safety at work (social dimension). This paper contributes to the dissemination of the theme and motivates the companies to implement sustainable maintenance

Prediction of compressor efficiency by means of Bayesian Hierarchical Models

The prediction of time evolution of gas turbine performance is an emerging requirement of modern prognostics and health management systems, aimed at improving system reliability and availability, while reducing life cycle costs. In this work, a data-driven Bayesian Hierarchical Model (BHM) is employed to perform a probabilistic prediction of gas turbine future behavior. The BHM approach is applied to field data, taken from the literature and representative of gas turbine degradation over time for a time frame of 7-9 years. The predicted variable is compressor efficiency collected from three power plants characterized by high degradation rate. The capabilities of the BHM prognostic method are assessed by considering two different forecasting approaches, i.e. single-step and multi-step forecast. For the considered field data, the prediction accuracy is very high for both approaches. In fact, the average values of the prediction errors are lower than 0.3% for single-step prediction and lower than 0.6% for multi- step prediction

Associations Between Building Information Modelling (BIM) Data and Big Data Attributes

There is a considerable effort to create and leverage digital data as the construction industry today relatively adopts innovative work processes. Among the innovative work processes getting traction these days, Building Information Modelling or BIM sits at the backbone of the industry's digital strategy with the capability to create a huge volume of digital data. Unveiling, the huge volume of digital data created by BIM processes could pave the way to leap the industry further. Big data is useful in this regard as a platform to derive potential insights from the accumulation of BIM digital data. Despite, a review carried out to understand the connection between BIM data and big data attributes suggests that the linkage between these domains is scantily mapped. Hence, a systematic mapping of these two domains is needed as a precursor for future research to establish the relationship between BIM data creation and big data progression. Using a systematic mapping approach, this paper aims to present an outcome from the analysis carried out to map both BIM data and big data attributes. The mapping analysis evidently suggests a noticeable connection among the two domains, mostly in operation and maintenance while the least is in the specification

Data size increment for fault detection on rotating machinery

En los últimos años se ha incrementado el uso de técnicas de modelamiento basado en datos para el diagnóstico de fallos en maquinaria rotativa. Estas técnicas requieren de grandes cantidades de datos que no siempre se pueden obtener pues generan  altos costos y tiempo excesivo, que son difíciles de solventar desde el punto de vista económico y técnico.  El presente trabajo se enfoca en el pre-procesamiento de las señales de vibración y propone un método para incrementar el número de series temporales informativas de una máquina rotativa sin el incremento del tiempo y costos en la etapa de adquisición de las señales. Como resultado se ha obtenido una ampliación de 315 señales en la fase de adquisición de datos a 429000 luego de la aplicación del método; cantidad adecuada para la construcción de modelos basados en datos, incluso de deep learning para la detección de fallos en maquinaria rotativa. Palabras clave: Adquisición de datos, pre-procesamiento, rodamientos, señales.  

Fault Prognosis System on Face-Mask Body Machine with Adabelief-Backpropagation Neural Network

Ultrasonic welding workload on vertical roller welding components on face-mask body machine in making masks has a high vibration of up to 20kHz. This high vibration causes the locking bolt to loosen the serrations, thus causing a greater failure of function, such as wear and tear on the teeth. If this function fails, it will cause downtime and high costs in the waiting process for the cleat component to be remanufactured. The damage prognosis system based on the condition of this machine implements a Classification of types of damage along with recommendations for maintenance activities that need to be carried out on the Face-Mask Body Machine. Classification of the type of damage to this system using There is a Belief-Backpropagation Neural Network (BPNN), a method for looking for weight settings on a neural network based on the error rate obtained in the previous iteration. This method is optimized using AdaBelief, which can adapt the step size based on the "confidence" of the previous gradient to get Convergence rates and generalization abilities better so that these types of problems can be known from the vibration signal of the machine which previously the signal was parsed using wavelet packet decomposition into frequency bands to obtain component data with low or high frequency. From the results of system performance testing, the modeling accuracy is 98.4%, so this system can be declared good and feasible to use in slack detection of vertical roller welding cleat fixing bolts

Key dimensions of effort for building information modelling data creation

While major industries begun to embrace technologies to improve revenue and productivity, construction industry appeared to be lagging in many aspects of technology adoption. Despite the industry’s straggle, there are several innovative work processes getting traction these days where Building Information Modelling (BIM) sits at the backbone of sector’s digital strategy with the potentiality to produce voluminous data. Fortunately, the expected accumulation of huge BIM data can bring potential insights in various digital platform through proper exertion of effort especially through big data. Though the associations between BIM data and big data has been theoretically established, the effort exertion in the creation of BIM data is still in the oblivious state. Therefore, a study to relate the dimensions of effort and various BIM data is needed as a precursor for future research. The outcomes evidently depict a recognizable linkage among them in which intensity is the least related dimension while most of the BIM data related with demand

Comparing different schemes in a combined technique of Kalman filter, artificial neural network and fuzzy logic for gas turbines online diagnostics

The paper presents research on the online performance-based diagnostics by implementing a novel methodology, which is based on the combination of Kalman Filter, Artificial Neural Network, Neuro-Fuzzy Logic and Fuzzy Logic. These methods are proposed to improve the success rate, increase the flexibility, and allow the detection of single and multiple failures. The methodology is applied to a 2-shaft industrial gas turbine engine for the automated early detection of single and multiple failures with the presence of measurement noise. The methodology offers performance prediction and the possibility of utilizing multiple schemes for the online diagnostics. The architecture leads to three possible schemes. The first scheme includes the base methodology and enables Kalman Filter for data filtering, Artificial Neural Network for the component efficiency prediction, the Neuro-Fuzzy logic for the failure quantification and the Fuzzy Logic for the failure classification. For this scheme, a performance simulation tool (Turbomatch) is used to calculate the thermodynamic baseline. The second scheme replaces Turbomatch with the Artificial Neural Network, that is used to calculate the deteriorated efficiencies and the reference efficiencies. The third scheme is identical to the first one but excludes the shaft power measurements, which are not available in aero engines or might not be usable for some power plant configurations. The paper compares the performance of the three methodologies, with the presence of measurement noise (0.4% reference noise and 2.0% reference noise), and 24 types of random single and multiple failures, with variable magnitude. The first methodology has been already presented by Togni et al. [10], whereas the other two methodologies and results are part of the PhD thesis presented by Togni [18] and they extend the applicability of the method. The success rate, targeting the correct detection of the of the failure magnitude ranges between 92% and 100% without measurement noise and ranges between 66% and 83% with measurement noise. Instead, the success rate of the classification, targeting the correct detection of the type of failure ranges between 93% and 100% without measurement noise and between 85% and 100% with measurement noise

Toward an integrated sustainability assessment in through-life engineering services

Through-life Engineering Services (TES) is comprised of develop, prepare, utilize and retire phases for complex engineering assets with a focus on maximizing their availability, predictability and reliability at the lowest possible life-cycle cost. TES employs a set of technologies and solutions to improve asset performance efficiently. On the other hand, optimal solutions for minimizing waste in terms of service time and resources is crucial for designing the right service at the right time. Thereby, specifying the possible TES opportunities within the economic, social and environmental sustainability dimensions can be an added value across different manufacturing sectors when deploying TES. However, due to the complexities and immensity of TES approaches, it is challenging to perceive such opportunities. To this end, the existing literature is limited to the effect of TES on economic sustainability and mostly focuses on investigating how TES has modified the service design to improve productivity and profitability. However, a comprehensive study on integrated sustainability has not been yet conducted. This paper presents a holistic view of the potential TES opportunities associated with the sustainability triple bottom line following a systematic review of empirical and theoretical advancements and methodological approaches in the literature. The outcome from this research raises the awareness of TES contribution in the design of sustainable service solutions and technologies, and offers a benchmark and reference point for future research in the field. Finally, this paper provides a set of recommendations that call for the further development of an integrated sustainability assessment framework for TES.Cranfield Universit

NOVELTY DETECTION FOR PREDICTIVE MAINTENANCE

Since the advent of Industry 4. 0 significant research has been conducted to apply machine learning to the vast array of Internet of Things (IoT) data produced by Industrial Machines. One such topic is to Predictive Maintenance. Unlike some other machine learning domains such as NLP and computer vision, Predictive Maintenance is a relatively new area of focus. Most of the published work demonstrates the effectiveness of supervised classification for predictive maintenance. Some of the challenges highlighted in the literature are the cost and difficulty of obtaining labelled samples for training. Novelty detection is a branch of machine learning that after being trained on normal operations detects if new data comes from the same process or is different, eliminating the requirement to label data points. This thesis applies novelty detection to both a public data set and one that was specifically collected to demonstrate a its application to predictive maintenance. The Local Optimization Factor showed better performance than a One-Class SVM on the public data. It was then applied to data from a 3-D printer and was able to detect faults it had not been trained on showing a slight lift from a random classifier

The application of a hybrid simulation modelling framework as a decision-making tool for TPM improvement

Purpose The purpose of this paper is to promote a system dynamics-discrete event simulation (SD-DES) hybrid modelling framework, one that is useful for investigating problems comprising multifaceted elements which interact and evolve over time, such as is found in TPM. Design/methodology/approach The hybrid modelling framework commences with system observation using field notes which culminate in model conceptualization to structure the problem. Thereafter, an SD-DEShybrid model is designed for the system, and simulated to proffer improvement programmes. The hybrid model emphasises the interactions between key constructs relating to the system, feedback structures and process flow concepts that are the hallmarks of many problems in production. The modelling framework is applied to the TPM operations of a bottling plant where sub-optimal TPM performance was affecting throughput performance. Findings Simulation results for the case study show that intangible human factors such as worker motivation do not significantly affect TPM performance. What is most critical is ensuring full compliance to routine and scheduled maintenance tasks and coordinating the latter to align with rate of machine defect creation. Research limitations/implications The framework was developed with completeness, generality and reuse in view. It remains to be applied to a wide variety of TPM and non-TPM-related problems. Practical implications The developed hybrid model is scalable and can fit into an existing discrete event simulation model of a production system. The case study findings indicate where TPM managers should focus their efforts. Originality/value The investigation of TPM using SD-DES hybrid modelling is a novelty