Predictive maintenance: fundamental in the company agenda

manutenzione predittiva

Predictive maintenance: Machine Learning and Artificial intelligence for a successful maintenance politicy in the company.

Predictive maintenance and data-driven approaches

In the last decade, there has been a lot of talk about Machine Learning and Artificial Intelligence applications in the context of company maintenance policies, so that they are considered the panacea for all problems.

Over time it was understood that there is no better policy than others. As far as maintenance is concerned, in fact, every company must try to find the best strategy which often does not consist in a single policy, but in a mix of different and correct strategies.

Predictive maintenance: the evolution of maintenance strategies over time

In the past few years, the interest in maintenance has never been central to the CEO’s agenda. However, after the pandemic, more company managers are turning their interest to this aspect, which has now become important.

manutenzione predittiva

The reason is clear: in the Covid era, the critical points of companies emerged more clearly: fragility and corporate rigidity. Hence the need of transformation into flexible and strong companies able to withstand sudden crises and emergencies. Not only that: strong companies are above all those that in addition to withstanding the emergency, are also able to take advantage of it.

Maintenance is no longer just essential to support operational continuity, but also as a solution to ensure production efficiency. Adapting profitably and efficiently to rapidly changing contexts is now more fundamental than ever.

Predictive maintenance: maintenance policies

Maintenance policy and historical evolution of maintenance engineering go hand in hand. Both, then, follow the level of knowledge of the system.

From corrective maintenance, which is not very effective for the efficiency of a plant, we have moved on to preventive maintenance on a statistical basis. That is the time to replace the component or system is established to avoid failure, which is based on the historical data collected and cost models. Of course, such a strategy is not suitable for accidental failures, but only for those due to wear.

In order to plan maintenance intervention in an economical and efficient way, the ability to collect health data of the plant following the various inspections and the ability to predict the remaining life is essential. Only by doing this is possible an effective maintenance policy.

Often these strategies are considered as maintenance cost reduction policies as their main objective is to avoid failure and minimize the impact on plant performance.

In recent years, many companies have understood the importance of investing in the acquisition of weak signals from systems, such as the current absorbed, the temperatures of the components, the variation in energy consumption and vibrations. All of this can be managed  thanks to the advent of technologies such as low cost sensors and cloud computing. This is how we arrived at the new predictive maintenance policies.

Predictive maintenance: data collection and the right balance between predictive policies

For many years Predictive maintenance has been discussed and practiced. But it is thanks to  increasingly high data collection capacity that predictive maintenance has been fully exploited.

What is the best way to generate profit? The first step is to proceed with a cost-benefit analysis. Without a doubt, corrective maintenance involves very low investments, but it can lead to very high costs for downtime or repair. In addition, there can be unpleasant cost consequences even for not predicting when the failure will occur.

Preventive maintenance, on the other hand, could lead to the inconvenience of a risky presumption of safety. Furthermore, this policy does not correspond only with artificial intelligence applied to maintenance. In fact, there are field predictive techniques and data-driven techniques.

controllo del mezzo

Both are excellent strategies to promptly identify a malfunction, however, field predictive techniques are more effective in detecting mechanical defects. In addition, field techniques are less tied to historical data than predictive ones.

Therefore, the post-Covid scenario brings maintenance to the top of priorities.

Whether it is corrective maintenance, predictive field maintenance or data-driven techniques in the directories’ agendas, maintenance is at the first place, now fundamental and essential.

The advantages and disadvantages of each of these strategies are now clear, just as it is increasingly clear that it is not a question of choosing one or the other, but a mix of each. Therefore, choosing the right balance between the different policies is the successful maintenance strategy.

Tamarri is always committed to identifying the most suitable and cutting-edge maintenance policy for your forklifts. 😉



Industrial IoT

industria IoT

Industrial IoT: five objectives to achieve within 2021 by elaborating datas in a comptetent way

Industrial IoT: Let’s clarify the data reading

Often due to the difficult interpretation, some entrepreneurs do not fully exploit what can be obtained from data and the value they can generate. A value often not clearly identifiable. Let’s try to clarify.

We assume that data is increasingly a strategic asset for any business. So it is clear that knowing how to decode them, understand them is important to be able to activate an entrepreneurial policy that takes into account their forecasts, which will make the difference.

Let’s analyze five aspects that are able to make the company competitive. These are extremely concrete aspects that can be achieved during the year. How? With a correct and competent analysis of the data generated by machinery through Industrial IoT solutions.

Five objectives for a competitive company

1. Ordinary maintenance

OEMs, i.e. Original Equipment Manufacturers (original equipment manufacturers) play a key role right now. To concretely assist their customers by encouraging rigorous and constant maintenance.

How? They Decline their know-how to the analysis of diagnostic data collected over time. This activity, which should become a real program, will produce value over time that will lead to an increasingly predictive, precise and effective analysis.

2. Know your customers

The analysis of data from IoT is a fundamental tool in order to learn about the machine life. Through the data it is possible to understand if the operators are using them correctly and how and when maintenance is done

This is how you can understand if there is room to use them even better to achieve maximum efficiency and therefore full customer satisfaction.

3.  Predictive problem analysis

Accessing the data allows greater proactivity towards the customer because it allows you to act before the assistance channels are activated.

How? By equipping the after-sales service with the tool able to monitor the connected machines. By doing so, it is possible to identify in time critical and alarming situations, unsafe working conditions or major failures

4. To Understand concretely how to improve the machines

Thanks to the correct use of analytical data it becomes less complex to understand how to improve the product in terms of mechanics and engineering. The technical and R&D department itself can also benefit from this.

5. Be more proactive towards the final customer

For every company is foundamental the need of manufacturers, which is to capitalize on product know-how effectively, thus improving knowledge of the machinery.

Only by doing so it will be possible to be closer to customers, with the common aim to increase and finalize the productivity and competitiveness levels.

To achieve this virtuous circle, an effective paradigm is to offer training on maintenance and process optimization, offering the possibility of remote diagnostic assistance and new methods of guarantees.

Tamarri S.r.l. is able to provide a complete solution in this area: discover our SMARTPASS 4.0 EVO platform for the complete management of the vehicle in  cloud.



SafeTS – anticcolision system

SafeTS – anticcolision system

We are very proud to present the video of our SafeTS (STS), the revolutionary platform dedicated to safety in the logistics sector!

STS is a platform, which aims to improve the safety of the logistics sector on handling vehicles, from the warehouse to the construction site. STS allows the recognition of the driver, the location of a pedestrian or other moving vehicles near the forklift and the slowing down of the vehicles within specific areas, representing an excellent aid to the safety systems already adopted. It also allows you to record all events (of management, driving performance, danger, collision) turning them into alerts easily accessible through mobile devices and Internet browsers.

SafeTSystem is very simple and feature-rich. With STS it is possible to:

Check access to the vehicle (Tkeylock function)
Check the presence of pedestrians (Tsafe function)
Automate the slowing down of the vehicle (Tslow function)
Automate the opening of unattended mechanical entrances (Topen)

It is also able to manage the vehicle safety signs (such as red / blue lights, buzzers, sirens)

The use of STS is simple and intuitive. The whole system is managed through SafeTApp, the specific app created for Android® devices that enables you to:

Parameterize all devices and transmitters
Check logs
Check events