Integrated Asset Management Consulting
Successful collaborations in the Asset Management domain start with a coaching project, to systematically plan and integrate the requirements of all internal and external stakeholders for a digital and predictive Asset Management System. We therefore poured our expertise in Asset Management into a consulting approach in which organizational change is focused on as a catalyzer on the way to a digital future. We coach, consult, and implement Asset Management change projects along this model.
The Institute of Asset Management (IAM) defines Asset Management as the following: “Systematic and coordinated activities and practices through which an organization optimally manages its physical assets, and their associated performance, risks and expenditure over their Life Cycle for the purpose of achieving its organizational strategic plan”.
The general goal and intention of a Maintenance Strategy should be to optimize the relation between the availability and criticality of a technical system and the overall cost of ensuring the availability goal.
Technical systems in the Corrective Maintenance Strategy will be operated until a major failure of a component leads to a shut down. Thus, the full utilization of the component life is guaranteed. However, the strategy can cause unscheduled and long downtimes and induces the risk of a subsequent failure. Synonyms for the Corrective Maintenance Strategy are break-down-strategy, run-to-failure strategy, or repair strategy.
The Scheduled Maintenance Strategy includes a cyclic policy in which components are serviced in fixed periods independent of their condition. The cyclic policy can be clock-based or age-based. Because the maintenance tasks are scheduled, planning possibilities are increased, especially when it comes to an efficient spare part management. Also, a high system availability is guaranteed applying this Maintenance Strategy. However, the utilization of the component lifetime or wear reserve is not fully guaranteed.
The primary goal applying the Condition-Based Strategy is finding the optimum point in time for maintenance actions. Data on the current condition of the component is collected by applying monitoring and inspection techniques on conditionally accessible wearing hot spots. Through early fault detection and following preventive measure, severe damage and damage propagation can be avoided. This results directly in lower repair and downtime cost but generates cost for installing and operating a monitoring and inspection system.
A variety of Predictive Maintenance Strategies have been developed. All have in common that reliability data and the probability of failure occurrence is used to optimize maintenance tasks. Applying probabilistic modelling allows the integration of various relevant data sources from field relevant for the deterioration status of the specific components – e.g. information from condition monitoring systems, combined with information from nondestructive testing techniques. Considering various data sources and not relying on single hot spot information makes Predictive Maintenance Strategies a powerful set of instruments to focus on optimal use of a component’s wear reserves. However, in practice, this is strongly dependent on the information quality and model uncertainties of the applied probabilistic models.
The performance of the chosen maintenance strategy combinations must be evaluated continuously along significant Key Performance Indicators (KPI). Which monitoring technique will be applied to analyze the KPIs is mainly cost-driven and can be evaluated individually in the framework of the holistic damage detection database framework. One of the main deficits are in many cases missing systematics in the overall Asset Management goal on a strategic and operative level, and in finding the optimal maintenance and spare part strategy. Naturally, economic performance is strongly dependent on the technical condition in which the turbine system and its components are, however, an Integrative Asset Management System must always consider the optimization behavior of technical maintenance and enhancement activities according to the economical dimension.
Successful organizational transformation concepts concentrate on the user perspective and implement all innovations with respect to specific Change Management aspects in the individual situations of the relevant organization. Change Management is key for success.
Current sensor technology for monitoring and inspection techniques should be analyzed according to their suitability in specific monitoring or inspection tasks, deployed, and integrated in a future Holistic Asset Management framework. Data acquired with sensor technology must be processed with specific algorithms, integrating all those views, and enable optimization in such a framework. To seek and find such optimal Asset Management Strategies, operators depend on adequate field information. Concerning this matter, current sensor technology for monitoring and inspection techniques should be analyzed according to their suitability in specific monitoring or inspection tasks, deployed, and integrated in a Holistic Asset Management framework. Data acquired with sensor technology must be processed with specific algorithms, integrating all those views, and enable optimization in such a framework.
A core element of an Integrative Asset Management System must be a holistic operational data base management system, which integrates all data streams relevant for operation and management activities of each Life Cycle phase. The core focus of a holistic operational data base management system for Asset Management purposes is the integration of information over the whole Life Cycle of assets and interdisciplinary cooperation and communication of data.