Health White Paper

Introduction

This publication contains the following topics:

Introduction
Concept
Health ecosystem
CHE operational workflow
CHE health
Relation between CHE structure and health signals
Definitions

Introduction

Are you a Terminal Manager and would you like to know how healthy your terminal is?

From a top management level, having a clear vision of how the assets are evolving might be challenging. Does my fleet operate efficiently? For how long can our equipment continue to operate safely? Is our equipment renewal strategy based on the right criteria? Was it worthwhile to move to this new technology?

At present these types of questions are often missing qualified answers.

If you are from an operations department, you will be interested in how healthy your TOS is but not only that. Organizing the daily operation which can deliver the best quality results requires more information. To get the most benefit from the CHE during operation, you need to anticipate. You probably also need to have a clear vision of your fleet from different perspectives in order to organize your equipment assignment properly, to get the best performance for the client.

E.g. is this CHE healthy enough to be used? Can I use it for the rest of the operation shift? When is this CHE supposed to be out of operation? How can I know when it is the best moment to free up the CHE for maintenance while keeping the optimum balance between my operational needs and that need for maintenance?

Once again, these types of questions are often missing qualified answers.

Then, if you are from the technical or maintenance department, it is obvious that the more feedback, diagnosis, and root causes that you can potentially receive, the better you will be at organizing your maintenance at the optimum time, minimizing the downtime during operation and moving from a reactive and preventive model to a predictive one. How healthy is the CHE? How healthy is that type of specific technology? How is my last structural inspection affecting my CHE health? How was over/under inflation pressure affecting the tyre life? What CHE signals should I take into consideration to optimize my maintenance operation? What is reducing my CHE life cycle the most and how can I prioritize maintenance tasks? How can I empower the communication layer between operations and technical departments with efficiency?

Again comes the most important point to get an answer to all this: data TRANSPARENCY, to qualified answers properly.

Health concept

The Terminal Industry Committee 4.0 (TIC4.0) aims to foster the development and adoption by the industry of common process semantics which enables the seamless and standardized data exchange among port equipment, digital platforms and other port terminal assets, thus advancing towards ‘plug & play’ solutions that can easily be deployed at port terminals.

TIC4.0 is a powerful language that can represent any reality that takes place at any cargo handling terminal in digital format.

With all of the different equipment and different systems to manage different tasks to resolve problems within Ports & Terminals, it can be difficult to have an overview of Port & Terminal Health.

By applying TIC 4.0 semantics to ‘Health control system’ as a Subject and “Healthy” as a Concept, we want to be able to transparently indicate Health on any topic, we will start by defining this upon the equipment level, as there’s typically already ‘health’ related info available.

Fast advances in information technology and in particular digitization, data analytics, and business intelligence have created new possibilities for the cargo handling industry, that could improve processes by connecting all equipment and systems in real-time, thus enabling seamless data exchange. Smart, prescriptive and predictive maintenance, KI supported troubleshooting, knowing which component is the optimum choice - our health concept will lay the foundation for more efficient troubleshooting and maintenance & repair approach across the industry (while considering various equipment types, manufacturers, ports and terminal operators).

Therefore, this concept aims

To create a health structure called ‘Health ecosystem’ across the various type of CHE
To create a taxonomy for health signals in normal operation
To create a taxonomy for diagnosis and possible root causes
To bring visibility on how maintenance or lack thereof is affecting the CHE health

The complete implementation of this concept will not only lay the foundation to utilize AI and deep learning in a very effective way to improve equipment health. It will also lay the foundation for performing more efficient maintenance and repair. Currently, technicians need to become knowledge experts of each manufacturer and their systems for failure reporting. Setting common standards will also make their work easier.

Usually, we assume health to be ok unless we feel or know that we have a health problem, or that we might expect a health problem in future (health prediction).

Typically, but not always, the equipment has warned and or stop lamps, error messages, and alarms to inform and support the operator to adequately adjust his behaviour and/or take care of the health issue reported.

Health ecosystem for added value

To gain all the benefits from health digitalization, data and machine learning, we have to break down the concept into different silos. Indeed, as health is a concept that can be seen from different perspectives and applied to different layers, it becomes natural to first draw the blocks that are concerned with CHE signals and maintenance.

We aim to represent CHE health in a digital way. As we assume health to be ok, we need to know what the errors, faults and warnings are at any time. This information is easy to collect from the CHE itself, but more detail needs to be collected to be certain of the health level.

Indeed, we’ll also need to collect the maintenance information that will provide us with the external inputs of CHE health.

Example:

I’ve got the tire health feedback saying that on SC25 tire no.1 is reporting a ‘low pressure warning’. Via the observed property of actual tire pressure we know it’s currently is 9.5 Bar. The last visual inspection on this tire as recorded in the MMS shows that the tire is damaged.

Is SC25 supposed to be out in operation?

In this example, it is easy to understand that using the CHE signals is not enough to define CHE health. External information has to be taken into account to drive the health status properly and can come from the maintenance system directly. We have to keep in mind that our aim is to digitalize health in the most reliable way.

The Terminal Industry Committee 4.0 (TIC4.0) aims to provide the right semantics and data models to first express the CHE Health based on CHE signals. The second step will be to provide the same for the maintenance health.

To create business value, different pieces of information need to be processed in the correct order to give feedback and knowledge about the CHE health level to make informed decisions about its operation or maintenance.

The following ecosystem gives an overview of the health system structure.

CHE operational workflow

Looking at a generic operational sample of the process whereby maintenance is happening during operation.

CHE Health

Looking at the individual asset and the relationship between asset health, monitoring and failure occurrence over time.

While looking at one single asset the following combination between the asset’s technical structure, health monitoring and failure occurrence is required.

Relation between technical structure and health messages

Managing a fleet

In a typical container terminal, a lot of different types of CHE are being utilized. The highest efficiency within the industry could be achieved by developing and implementing a taxonomy which uses the same TIC-ts-code for the same functions irrespective of OEM.

How to distinguish between error, warning and fault

Error = An error is a difference between actual output and expected output.

Warning = Warning is a condition in which the subject is close to failure on a required function, or a certain condition is not met in order for the subject to function normally. It is required to effect decision-making over the subject.

Fault = Fault is a condition that causes the subject to fail to perform a required function.