Operational risk assessment model for marine vessels

The changing nature of manufacturing, in recent years, is evident in industry’s willingness to adopt network-connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision-making systems. While e-maintenance practice provides aframework for internet-connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of acomprehensive framework for its processing, analysis and use should be avaluable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data, allowing future systems to enable ‘Human in the loop’ interactions.

/pdf/intelligent-decision-support-for-maintenance-an-overview-and-10xl2c7g4s.pdf

Intelligent decision support for maintenance: an overview and future trends

A typical mining company has three important assets: the human labor-force, the orebody, and the equipment. Trucks, excavators, drilling machines, crushers, grinders, classifiers, and concentrators represent the equipment. Mining operations that want to take advantage of economies of scale have huge equipment fleet, and the worth of the equipment could be more than a hundred million dollars. The reliability and availability of this equipment play critical roles in increasing the efficiency and productivity of a mining operation. This paper proposes an effective maintenance management approach to be used in the mining industry such that equipment availability and reliability are improved while potential failures are prevented. Using failure data of a mining truck fleet in an open-pit Canadian mining operation, a case study is conducted in two steps. The first step focuses on determining optimal inspection intervals based on the desired reliability level in such a way as to detect potential catastrophic failures that represent considerable maintenance and downtime costs. The second step develops a preventive maintenance scheduling plan that differentiates between physical and virtual age considering the degradation of systems and their rejuvenation after each repair. The research outcomes show that the proposed approach has the potential to increase the benefit obtained from the mining equipment and can be used in mining operations.

Optimal Inspection and Preventive Maintenance Scheduling of Mining Equipment

A three-stage failure process divides a product’s life into three stages: normal, minor defective and severe defective stages. Under the condition-based renewable warranty policy, inspections are c...

Optimal condition-based renewable warranty policy for products with three-stage failure process

In this article, we deal with optimal preventive maintenance policies based on online condition monitoring. The failure rate function is important for maintenance decisions. Two concepts of failure are proposed and the computing method based on condition monitoring data is given. Both un-repairable and repairable equipment are taken into consideration. For repairable equipment, the degree of degradation and failure rate will decrease after maintenance. The result of the simulation shows that taking the two types of failure rate functions into account will make the expected cost rate less than the classical method. So we draw a conclusion that the two types of failure rate functions are advantageous in maintenance decisions.

Preventive Maintenance Policies for Equipment Under Condition Monitoring Based on Two Types of Failure Rate

This paper studied the imperfect maintenance policy for a two-unit system with failure interactions. The improvement factor concerning about the age and maintenance cost of the system was newly created to describe the effect of the maintenance. The numerical results and case study were also included to show the validity of the models.

http://ieeexplore.ieee.org/iel7/6599406/6625514/06625678.pdf

Notice of Retraction Imperfect maintenance policy for a two-unit system with failure interactions based on improvement factor

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Preventive Maintenance Policies for Equipment Under Condition Monitoring Based on Two Types of Failure Rate

Notice of Retraction Imperfect maintenance policy for a two-unit system with failure interactions based on improvement factor