1.   Introduction

The publication TIC4.0 2023.009 covers the following topics:

• White Paper “Maintenance” ensuring “Health”: After defining in the last Release (TIC4.0 2023.008) what Health means, this time actions were focused on the subject Maintenance, defined as a group of actions that take place to preserve the asset’s Health. A distinction on the Types of Maintenance (Corrective, Preventive and Predictive) has been made. To be as closely related to reality as possible, the structure closely follows the processes needed and reflected in an Asset Management System.

• Maintenance Data Model: A new Data Model for Maintenance has been developed to reflect the processes, concepts and definitions that concern this area of interest.

• KPI White paper: This paper intends to create an understanding of the methodology of how any KPI (standard or exotic) may be calculated and represented using the TIC4.0 semantic. KPI = Represents in a consolidated way a reality, during a period of time, filtered, split and grouped in such a way that allows the reader to understand it creating value.

• Cargo: The definition has been expanded to reflect and cover Reefer-Cargo characteristics ‘TypeReefer’. Furthermore, the status of “unpowered, plugged and unplugged” were defined as well.

• Spreader: New concepts related to different parts of the spreader (tools, extensions) have been the focus of this latest release. Furthermore, definitions of specific tools of the spreaders (grapple arm, legs, etc.) and parts of the unit have also been developed in this release.

• General: Concepts like On, Off and Powered were already defined in the previous releases for the “Container Handling Equipment (CHE)”, now they have been updated for “reefer” and moved to the “General” section.
  
  

“Maintenance” ensuring “Health”
Maintenance is a cornerstone of industrial operations, ensuring the health, efficiency, and longevity of equipment and processes. In the industrial context, maintenance is not just a routine task, it's a strategic imperative for preventing disruptions and optimizing performance.
Regular maintenance, including inspections, lubrication, and component replacement, prevents breakdowns, reduces downtime, and extends the lifespan of equipment.
In TIC 4.0 semantics, Maintenance as a subject could be summed up as an action taken to keep the health of an asset at the original level. It can be broken down into different segments depending on the level of the performed action and/or the time when this action is performed. We then have:
Corrective maintenance groups all the actions taken after a detected issue. Corrective maintenance can also be split up into:

• Diagnosis when teams are investigating a problem,

• Repair when it comes to the time to organize the work around a problem in order to resolve such and let the asset achieve what it is designed for,

• Preventive maintenance groups all the actions that must be done during the lifetime of the asset at a specific time and according to a specific sequence,

• Predictive maintenance is more advanced and based upon specific measurements to decide if we have to go for a corrective action that can be planned. It aims to optimize the maintenance schedule and reduce downtime by detecting faults before a problem happens

TIC4.0 commits to digitalize the maintenance activities to empower our knowledge of the equipment and to anticipate downtime in the most accurate way. The Asset/Maintenance data model surrounds the communication and collaboration in maintenance processes while fostering a better representation of the collaborative and sequenced operations.

KPI White Paper

This paper intends to create an understanding of the methodology of how any KPI (standard or exotic) may be calculated and represented using the TIC4.0 semantic. By using this semantic, the TIC4.0 user is empowered to create his/her own KPI which best suits the user’s requirements.
This paper does not intend to re-create existing KPIs such as “crane productivity” or “terminal throughput”.

However, it illustrates how to use TIC4.0 semantic to represent such common KPIs and provides examples with 5 simple and popular KPIs. This list of examples will be extended in future releases.

KPI = Represents in a consolidated way a reality, during a period of time, filtered, grouped and split in such a way that allows the reader to understand it creating value.

2.   Data Model

For the digital formatting of the semantic and Dataset we need a Data Model to structure the data and a Data Schema to define the details of the content such as the validity of the format, the type of data (Boolean, entire, real etc.), which data is mandatory or could be omitted etc.

The Dataset has been defined based on the RDF Resource Description Framework using the subject->predicate->object schema.

Following the semantic web standard (subject: object) the model has 3 main components: header, asset description and measurement.

Open

SUBJECT creates the hierarchy tree structure (we have sub-subjects) that helps to identify the boundary of the value. The hierarchy is fixed by TIC4.0 for each kind of subject (CHE, TOS, Terminal) and can mix any type of subjects (e.g. machine.process = che.move). The subjects conform to an array defined by the (concept) metadata so various identical subjects but with different metadata (id or name or location or…) can be sent in the same message. (one message with several CHE's or one CHE with several spreaders).

The CONCEPT's metadata defines ‘what is’ and the CONCEPT ‘what does’. Both are flat (no hierarchy, no arrays) and as many as necessary can be used. Additionally, two concepts can be combined with ‘and’ or ‘or’ creating a new concept which includes the condition that makes both true. For e.g. ‘hoisting_and_trolleying’ that represents the action of hoisting and trolleying at the same time (both statuses must be true).

OBSERVED PROPERTIES define the ‘magnitude’ of the CONCEPT, are flat (no hierarchy) and can be used as many times as necessary with a CONCEPT.

For each OBSERVED PROPERTY, an array created by the combination of the different POINT OF MEASUREMENTs in time (actual, estimated, etc), place (input, iinput, ioutput, output), timestamps and the different Units will give an array (a list) of VALUEs. The array could be if necessary in each message. The length will depend on the relation between the data frequency and the message frequency and also the amount of different POINT OF MEASUREMENTs that need to be represented.

A detailed definition of the Data Model can be found in Data Model.

The Dataset is the content of the Data Model, a flat version without hierarchy or rules. The Dataset is used by humans, but machines need the Data Model and the Data Schema to translate it to a digital format. 

3.  Generic Documentation

In this release, the following generic documents and information are available:

4.  Definitions

The following definitions have been created or modified in this 2023.009 publication:

Generic Definitions

Reefer

Spreader