ANÁLISIS BLOCKMODEL

The pressure on cost reduction increased the importance of monitoring performance, above all maintenance performance. It is widely acknowledged that it is necessary to support maintenance staff by supplying them with accurate and up-to-date information regarding maintenance tasks and recent history.

In order to do this, it is important that the necessary data is captured, stored and presented in an appropriate way. Asset management and condition data can be captured either manually or with sensors but this potential wealth of information must be effectively managed if it is to be of use (Baglee et al., 2012).

Baglee et al. (2012) identify a cyclic process in managed data:

 Data Gathering

 Data Storage

 Data Analysis

 Data Presentation

Data gathering: collecting data is crucial for both scheduling maintenance and for assessing the performance of maintenance regime. This can be done through manual inspection or online sensors, and may regard also cost data for both maintenance and repairs followings failures, usage statistics and downtime costs resulting from both maintenance and repairs.

Many condition sensors are available ranging from simple temperature probes, vibration sensors, and pressure transponders to advanced systems such IR spectrometers for automatically measuring the condition of lubricating oil. Other important condition monitoring technologies include vibration analysis, acoustic emission analysis, thermography and mechanical stress measurement. These condition-specific techniques can be supplemented with the analysis of other general, functional characteristics of the equipment, such as flow rates, temperatures and

Maturity assessment for Physical Asset Management: Evidence from Manufacturing Plants and Infrastructures

pressures. Furthermore, energy consumption is increasingly finding applications as an indicator of condition.

These automated techniques can also supplement manual data collection using devices such as PDAs (Personal Data Assistant) and hand held data collectors. The increased memory capacity computational power of mobile phones will lead to them finding application in this area (Fumagalli et al., 2010)

Data storage: once data are collect, it is important to store and manage them in a manner which makes it easily accessible and which allows the required data to be accessed efficiently when required.

Database systems offer many advantages in the field of asset management and maintenance in terms of security and data accessibility. Database systems are then an essential part in order to achieve data storage, and may find many different forms in maintenance application, ranging from general purpose tools (i.e. tools to develop data bases commercially available in PC platforms, such as e.g. Microsoft Excel and/or Access) to being core part of so called CMMSs (Computerized Maintenance Management System) or software modules of more general ERP systems.

To better integrate different systems needed in maintenance application, ranging from shop-floor / equipment to business level, a number of standards has been proposed.

Such standards are the main background in order to develop systems’ interoperability in maintenance, they may be both generic (such as e.g. XML) and specific standards (MIMOSA for condition based maintenance, …).

Data analysis: analysis of data are fundamental to identify maintenance areas where improvements are possible or necessary.

Several methodologies exist to maximize the performance of a maintenance regime based on an analysis of the performance of the assets and the existing maintenance regime. The primary methodologies are Total Productive Maintenance (TPM) and Reliability Centered Maintenance (RCM), with variations being developed to suit individual organizations. TPM/RCM include then different methods for data analysis such as, e.g., histograms, Pareto diagrams and RCA (root cause analysis) originally used in TPM, Weibull analysis and RBD (Reliability Block Diagram) promoted in the frame of RCM.

Maturity assessment for Physical Asset Management: Evidence from Manufacturing Plants and Infrastructures

Data analysis is also important in terms of extracting meaningful information from raw sensor data using statistical techniques. The goal of any statistical analysis is in fact to uncover facts. Various tools are available for data processing and analysis to this end. The most widely used techniques are Artificial Neural Networks, Statistical Learning and Probabilistic Modeling (Baglee et al., 2012). Most fault detection systems function by studying the relationship between various sensor readings and using some form of model to detect abnormal behavior.

Data presentation: presenting data in a convenient and appropriate way is vital in ensuring that asset management systems are used effectively and provide a return on their investment.

Having a good data presentation system means providing the correct data to the correct personnel. For example, management personnel require a high level overview indicating current performance across an appropriate reporting period while shop-floor maintenance engineers require current data relating to the operations assets which are their responsibility.

PDAs, other mobile devices (such as smart phones) and Dashboard Interfaces are very useful tools for this aim, i.e. in order to achieve a customized presentation of data/reporting to the user. In particular:

• PDA technology supports the transfer of data between the user and a central maintenance database system. The role of the PDA is to provide a user-friendly, comfortable and powerful mobile computing device for dealing with different types of data processing and maintenance activities.

PDA can be used in a different way, relating to the needs of the users. For example, technicians can follow the onscreen instructions step-by-step to complete a maintenance task. Even in poor conditions where no network connection is available, PDAs will become more useful since a compact database can be pre-stored inside the PDA’s internal memory or memory stick.

• Evolution of PDA and mobile phone technology are smart-phones, that are available at relatively low cost and offer the ability to run easily-written software. Furthermore they are increasingly equipped with high level systems such as GPS receivers and broadband connectivity. Smart-phones also feature calendar and organizer systems which can be integrated with bespoke

Maturity assessment for Physical Asset Management: Evidence from Manufacturing Plants and Infrastructures

software. These features, coupled with steadily growing memory capabilities, make smart-phones the likely replacement for PDAs as mobile maintenance management tools, especially in applications where remote maintenance is required due to their mobile connectivity.

• Dashboard system is an “user interface that is to be easy to read” (Wikipedia, Dashboard (management information systems), 2012). It has been a well covered topic in many areas of decision support. These systems provide, for example, management personnel with only the most essential information required for senior managers to assess. Also technical personnel may find the required data presentation: simple graphical displays are used in order to illustrate and present, e.g., power consumption, maintenance and condition monitoring data with click through access to greater detail and analysis as required.

Fig.2.12 summarizes the previuos concepts.

Fig.2.12 Asset Management tools by position in data lifecycles from Baglee et al., 2012