Analyzing Critical Failures in a Production Process: Is Industrial IoT the Solution?

Abstract

Machine failures cause adverse impact on operational efficiency of any manufacturing concern. Identification of such critical failures and examining their associations with other process parameters pose a challenge in a traditional manufacturing environment. This research study focuses on the analysis of critical failures and their associated interaction effects which are affecting the production activities. To improve the fault detection process more accurately and efficiently, a conceptual model towards a smart factory data analytics using cyber physical systems (CPS) and Industrial Internet of Things (IIoTs) is proposed. The research methodology is based on a fact-driven statistical approach. Unlike other published work, this study has investigated the statistical relationships among different critical failures (factors) and their associated causes (cause of failures) which occurred due to material deficiency, production organization, and planning. A real business case is presented and the results which cause significant failure are illustrated. In addition, the proposed smart factory model will enable any manufacturing concern to predict critical failures in a production process and provide a real-time process monitoring. The proposed model will enable creating an intelligent predictive failure control system which can be integrated with production devices to create an ambient intelligence environment and thus will provide a solution for a smart manufacturing process of the future.

Arquitectura de referencia para el diseño y desarrollo de aplicaciones para la Industria 4.0

“…Wingerath et al (2016) propusieron kappa y lambda como soluciones arquitecturales centradas en el dato y basadas en buses de datos para hacer frente al procesamiento de flujos de datos en tiempo real. Estas han sido utilizadas en el ámbito de la I4.0 por Arantes et al (2018) para desarrollar una herramienta de análisis de datos para la I4.0; por para sensibilizar a los usuarios domésticos sobre el consumo de energía sostenible y por Ahmad et al (2018) para construir un modelo para minimizar la ocurrencia de fallos en las máquinas de una cadena de producción, entre otros.…”

The Value Cocreation Influence Mechanism of Network Freight Transport Platform in IoT-Based Environments: Under the Service-Dominant Logic

“…SDL defines service as "the ability to apply for the benefit of others," which is regarded as the 3 Wireless Communications and Mobile Computing theoretical basis for the development of service science and the research of the service system [8]. The main body of VCC uses the service system to provide resource sharing, including information sharing, work sharing, risk sharing, and commodity sharing [37], and realizes the connection and interaction between the parties through the ability of the service system (such as knowledge, skills, and technology) [38]. Under the common value proposition, VCC is realized through resource complementarity and ability integration [39].…”

Identifikasi Resiko Kegagalan Proses Penyebab Terjadinya Cacat Produk dengan Metode FMEA-SAW

“…Kualitas hasil produksi dipengaruhi oleh berbagai faktor, diantaranya yaitu kualitas bahan baku [4] dan kondisi proses produksi [5]. Gangguan ataupun kegagalan yang terjadi dalam proses produksi dapat menyebabkan dampak negatif pada proses yang lainnya ataupun hasil produksi [6].…”