equipment failure
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This case involved industrial equipment whose repeated, seemingly random failures resulted in the buyer of that equipment suing the seller. The failures had been isolated to a group of several transistors within electro-mechanical modules within the equipment, but the root cause of those transistors failing had not been determined. The equipment seller had more than 1,000 units in the field with no similar failures. And the electro-mechanical module manufacturer had more than 20,000 units in the field with no similar failures. Electrical contractors hired by the buyer had measured power quality, and reported no faults found in the three-phase power at the equipment terminals. This paper presents circuit analyses of the failing electro-mechanical module, basics of electrostatic discharge damage and protection, and the root cause of these failures — an electrical code-violating extraneous neutral-to-ground bond in a secondary power cabinet.

Accurate prediction of airborne equipment failure rate can provide correct repair and maintenance decisions and effectively establish a health management mechanism. This plays an important role in ensuring the safe use of the aircraft and flight safety. This paper proposes an optimal combination forecasting model, which mixes five single models (Multiple Linear Regression model (MLR), Gray model GM (1, N), Partial Least Squares model (PLS), Artificial Neural Network model (BP), and Support Vector Machine model (SVM)). The combined model and its single model are compared with the other three algorithms. Seven classic comparison functions are used for predictive performance evaluation indicators. The research results show that the combined model is superior to other models in terms of prediction accuracy. This paper provides a practical and effective method for predicting the airborne equipment failure rate.

Based on the analysis of bacterial parasitic behavior and biological immune mechanism, this paper puts forward the basic idea and implementation method of an embedding adaptive dynamic probabilistic parasitic immune mechanism into a particle swarm optimization algorithm and constructs particle swarm optimization based on an adaptive dynamic probabilistic parasitic immune mechanism algorithm. The specific idea is to use the elite learning mechanism for the parasitic group with a strong parasitic ability to improve the ability of the algorithm to jump out of the local extreme value, and the host will generate acquired immunity against the parasitic behavior of the parasitic group to enhance the diversity of the host population’s particles. Parasitic behavior occurs when the number of times reaches a predetermined algebra. In this paper, an example simulation is carried out for the prescheduling and dynamic scheduling of immune inspection. The effectiveness of prescheduling for immune inspection is verified, and the rules constructed by the adaptive dynamic probability particle swarm algorithm and seven commonly used scheduling rules are tested on two common dynamic events of emergency task insertion and subdistributed immune inspection equipment failure. In contrast, the experimental data was analyzed. From the analysis of experimental results, under the indicator of minimum completion time, the overall performance of the adaptive dynamic probability particle swarm optimization algorithm in 20 emergency task insertion instances and 20 subdistributed immune inspection equipment failure instances is better than that of seven scheduling rules. Therefore, in the two dynamic events of emergency task insertion and subdistributed immune inspection equipment failure, the adaptive dynamic probabilistic particle swarm algorithm proposed in this paper can construct effective scheduling rules for the rescheduling of the system when dynamic events occur and the constructed scheduling. The performance of the rules is better than that of the commonly used scheduling rules. Among the commonly used scheduling rules, the performance of the FIFO scheduling rules is also better. In general, the immune inspection scheduling multiagent system in this paper can complete the prescheduling of immune inspection and process dynamic events of the inspection process and realize the prereactive scheduling of the immune inspection process.

Abstract Objective/Scope One of the main concerns of Oil & Gas Plants and associated Buildings is how to improve indoor air quality (IAQ) and tackling viruses. IAQ can be affected, or may become under high risk by some of nearby gases, microbial contaminates or energy stressor that affect the HSE condition. This paper presents the main factors that been considered to provide practical solutions to achieve high IAQ and tackling viruses (such as COVID-19). Methods, Procedures, Process IAQ refers to the air quality within and around the plants/buildings. IAQ can usually be affected, or may become under high risk by nearby gases, particulates, microbial contaminates or any mass that affect 100% HSE. Inadequate air quality in building will increase the risk and impact on transferring viruses to people (such as COVID and Flue) and equipment performance (such as equipment failure, components corrosion and short circuits on control board). Survey and data was recorded to evaluate air quality performance in atmosphere instead of assuming it. Accordingly, the impact of inadequate IAQ was studied and evaluated. Results, Observations & Conclusions The international standard set a good IAQ in respect of gas concentration and human who works inside buildings in a way that less than 50% people should not detect any odor, 25% should not experience discomfort, 10% should not suffer from mucosal irritation and 5% should not experience annoyance. Study concluded that inadequate IAQ inside the building will affect people performance/health and installed equipment performance. In addition, improper HVAC system operation will be become breeding site for odor causing mold and bacteria, specifically on cooling coil. Hence, several technics were studied to improve IAQ, by installing Ultraviolet (UV) light to stop growing bacterial inside the HVAC system, installing chemical filter in air intakes to remove atmospheric dust, gases and bacteria by 100%, upgrading filtration efficiency to MERV-13 or highest achievable to capture at least 75 – 95% of airborne particles between 0.3 and 1.0 micron, increase outdoor air ventilation and temperature/humidity control. The performance of HVAC system and quality of air inside building were monitored by simulating IAQ based on ISO 16890, filters life cycle, energy consumption, and the results were found 100% satisfactory and provided solutions that are now successfully implemented in all new and some of the existing buildings. Novel/Additive Information There are several buildings with similar issues and these approach/technics now being adopted in new constructed/existing buildings to protect human and asset integrity, which will support ADNOC Way by sustaining safe environment operation, lower health risk, reduce of equipment failure, reduce maintenance cost and 100% HSE. There are numbers of occupied buildings across the world were surrounded by aggressive gases/pollution with poor IAQ and above approaches it can be followed to realize larger benefits.

One of the consequences of globalization was an increase in the response between companies. In this way, companies increasingly seek continuous improvement using various mechanisms and tools aimed at improving their processes, products, and services, thus making them more competitive. In this scenario, FMEA stands out as a unique tool for detecting and reducing failures in products and processes in order to improve their confidence. This article is the result of the study of the application of the FMEA in an equipment to the production process of insertion of components. For this, action research was carried out, where equipment failure data was collected. As a result, a 5W2H action plan was prepared with the support of a GUT matrix to apply the recommended improvements in the FMEA.

Penelitian ini bertujuan untuk mengetahui peningkatan efektivitas mesin coal crusher dengan menggunakan analisis deskriptif dan kuantitatif metode six sigma. Penggunaan Overall Equipment Effectiveness dan Six Big Losses untuk menentukan nilai Overall Equipment Effectiveness dan faktor dominan yang menyebabkan rendahnya kinerja mesin atau peralatan. Proses penerapan small group activity sebagai bagian dari Total Productive Maintenance dengan penerapan fase DMAIC dari analisis Overall Equipment Effectiveness (OEE), Six Big Losses, dan Fish Bone Diagram untuk menganalisa penyebab masalah yang menghambat kinerja mesin dan peralatan serta signifikansi peningkatan kinerja operasional di departemen FPM. Berdasarkan pengolahan data pada Coal Crusher dengan metode OEE dan Six Big Losses faktor yang paling dominan menyebabkan rendahnya kinerja mesin adalah Reduced Speed Losses dengan persentase 58,82% dan Equipment Failure Losses dengan persentase 62,21% pada mesin Coal Crusher yang mempengaruhi efektivitas dan produktivitas mesin  tersebut. Menggunakan diagram pareto untuk menganalisis data losses yang ada sehingga akar permasalahan dapat ditemukan. Selanjutnya dengan analisis fish bone diagram didapatkan saran-saran langkah pencegahan dalam mengurangi jumlah losses time. Langkah perbaikan sistem yang dilakukan di Departemen FPM dengan penerapan Small Group Activity sebagai control secara berkelanjutan memberi peningkatan produktivitas terhadap kinerja Coal Crusher di akhir periode Desember 2020 yaitu diperoleh rata-rata nilai Overall Equipment Effectiveness (OEE) yaitu sebesar 87% dan telah memenuhi nilai standar dunia 85%. Dengan adanya peningkatan kinerja diharapkan kegiatan proses produksi dapat berjalan efektif, efisien, serta energi dapat digunakan secara optimal.

The first book on a promising new direction — methodology for equipment failure management for technical managers and specialists of industrial enterprises, as well as for all those whose field of expertise includes the need to deal with equipment failures — has been developed and published under the auspices of the Association for Effective Enterprise Asset Management (EAM Association).

Abstract The paper analyses the causes of emergency outages on electric networks according to the data of the Pravoberezhny district of Irkutsk for 2008-2017, according to which 82% of failures of elements of the electric network are caused by breakdowns and equipment failure; 9% are due to the influence of external factors; 5% occurred due to natural and climatic reasons, 4% – due to other circumstances. The largest number of outages due to operational reasons occurs due to damage to cable and overhead lines. Accidents caused by the influence of external factors are mainly associated with the negative impact of people, animals and birds. Equipment failures related to natural and climatic factors are mainly due to wind or thunderstorms. Other circumstances, as a rule, include staff errors and unidentified reasons. Calculations of undersupply of electricity and economic losses caused by interruptions in power supply were made. It should be noted that in order to reduce the number of equipment failures, it is necessary to carry out timely maintenance and reconstruction of various elements of electrical networks.

Transformers make up the main electrical network of the equipment. Failure of a transformer leads to loss of income and affects the reliability of the power supply for consumers. In order to ensure long and trouble-free operation of the transformers, various diagnostic tests are performed and corrective actions are initiated throughout their lifetime. There are many methods to access the residual life of transformers, for example short circuit tests on transformers and areas of application.