7 IoT trends for machine maintenance in 2025
IoT technologies make machine maintenance and servicing more efficient, cost-saving and predictive. Companies can reduce unplanned downtime by up to 50% and cut maintenance costs by up to 40%. Here are the 7 key trends that will revolutionize maintenance in 2025
- AI-supported failure prevention: Detect faults early and accurately predict remaining service life.
- Edge computing: On-site data processing for real-time analyses with latencies of less than 10ms.
- Extended sensor networks: more precise monitoring and fewer false alarms.
- Automated maintenance systems: Autonomous fault diagnosis and repair processes.
- Energy consumption monitoring: more efficient use through real-time analysis.
- IoT design with a focus on security: protection through encryption and security chips.
- Retrofit solutions for older machines: Retrofitting existing systems with IoT functions.
Comparison: traditional vs. IoT-supported maintenance
| Aspect | Traditional | IoT-supported |
|---|---|---|
| Machine availability | 85-90 % | > 95 % |
| Maintenance costs | High | 30-50 % savings |
| Energy saving | Hardly available | 8-12 % |
| Troubleshooting speed | Slow | Up to 83 % faster |
The future of maintenance is digital, data-based and secure – with clear benefits for efficiency and resource conservation. IoT technologies open up opportunities for new “smart” service products, especially for machine manufacturers. Properly exploited, the use of these IoT capabilities generates more service business and increases customer loyalty.
Maximizing Asset Value with Predictive Maintenance at the Edge
In this presentation at the Field Service Digital Summit 2020, Software AG discusses where and how edge analytics are being used and how utilizing predictive maintenance can help improve operations by making them more efficient and effective.
1. AI-supported failure prevention
AI-supported systems are changing machine maintenance by detecting faults at an early stage and making precise predictions about the remaining service life of components. This technology is based on condition-based strategies and automates preventive measures. In practice, such systems achieve 92% accuracy in predicting the remaining service life of components [3].
One example is Sensosurf, which uses machine learning (ML) to analyze wear patterns in rotating equipment. This is done using vibration data and thermal images [2].
The benefits of modern AI systems are impressive: they reduce unplanned downtime by 30-50%, cut maintenance costs by 20-40% and deliver predictive accuracy of over 90%, often with less than 72 hours’ notice.
Such systems are integrated via edge AI gateways. These process sensor data locally, while cloud-based ML platforms perform more complex analyses. This edge-cloud architecture also forms the basis for real-time analyses made possible by edge computing.
Thanks to standardized interfaces, PLC data can be combined with new IoT sensor inputs. Modular middleware facilitates integration [11][8]. Federated learning also plays an important role: it allows the exchange of knowledge between companies without revealing sensitive competitive advantages – a decisive factor in the IoT market [3].
The accuracy of the AI models is ensured by comparing them with digital twins. Automatic updates, which are carried out every 72 hours, ensure continuous precision [2].
2. Edge computing for real-time analyses
Edge computing fundamentally changes machine maintenance by processing data directly at the machine’s location. This reduces latency times from 100-200ms to less than 10ms – a decisive advantage for critical maintenance processes [1][3]. This technology not only supports the AI systems described in Trend 1, but also opens up new application possibilities.
A practical example: reaction times for cooling systems have been reduced to less than 8 minutes [8]. Modern edge architectures rely on powerful hardware such as the OnLogic ML100-GX9, combined with OPC UA protocols and InfluxDB databases [3][8]. The APAS Inspector from Bosch demonstrates how powerful these technologies are by enabling image analysis in under 50ms [2].
“Edge computing is becoming a game changer for industrial real-time decisions – especially for safety-critical applications.” – Matthias Breunig, McKinsey Partner [2]
According to Fraunhofer studies, edge computing has clear advantages: Responses to anomalies are 63% faster, and component lifetimes are extended by 22% through optimized load balancing [3][7].
The technology also makes it easier to modernize existing systems – a topic that is covered in more detail in Trend 7. Security issues, such as the use of TPM 2.0 chips, are examined in detail in Trend 6.
3. Extended sensor networks
Today’s sensor networks offer precise monitoring of several parameters simultaneously. One example is Sensosurf’s force sensors, which can measure vibrations, temperature and lubrication quality in one system [2]. This networked data acquisition is crucial for the optimization of energy consumption discussed in Trend 5.
The results of these systems speak for themselves: micro-anomalies in rotating parts are detected 30-50% faster than with classic single-function sensors [1][2]. In combination with edge computing (see trend 2) and wireless mesh networks, the false alarm rate is reduced by 40% [11].
| Key figures for modern sensor networks | Average improvement |
|---|---|
| Unplanned downtime | -18% |
| Manual inspections | -50 to -70% |
| Warranty costs | -18% |
A look ahead to 2025 already reveals exciting developments: Self-calibrating MEMS sensors are expected to increase accuracy by ±0.1% [2]while battery-free energy harvesting sensors open up new possibilities [8]. In addition, integrated AES-256 encryption reduces security risks by 60% – a topic that is discussed in more detail in Trend 6 (Security-First IoT Design).
4. Self-running maintenance systems
IoT technology is making maintenance increasingly autonomous and efficient. One example: Siemens MindSphere. In 17 plants, vibration sensors automatically monitor motor imbalances and start dynamic balancing processes if necessary. The result? 12,000 fewer manual inspections per year and production downtime worth 4.2 million euros avoided [1]. These systems rely on the sensor networks described above to use real-time data for autonomous decisions.
IoT-controlled fan systems are another example. They continuously monitor bearing temperatures and lubricant quality. If the values exceed defined limits, they automatically start cleaning cycles. This reduces on-site operations by 40% and increases the operating time between failures by 28% [6].
| Automated maintenance function | Average increase in efficiency |
|---|---|
| Fault diagnosis & rectification | 60% faster resolution time |
| Spare parts management | 25% lower storage costs |
Collaborative robots also play a role here. Fanuc’s CRX robotic arms automatically adjust belt tensions, while Boston Dynamics’ ‘Spot’ robot carries out inspections in hazardous radiation zones – controlled by IoT vibration data [2][4]. This shows how AI analysis (see trend 1) optimizes physical maintenance processes.
“In 2025, hybrid AI systems will combine pattern recognition with logical problem solving to autonomously maintain complex systems.” – Kristian Kersting, Professor at the TU Darmstadt [14]
Such systems are being introduced step by step. IoT gateways such as Advantech WISE-500 form the basis, digital twins simulate failures and algorithms for failure mode analysis are linked to ERP systems for automated spare parts logistics [11][4].
A look into the future: Siemens Industrial Copilot uses generative AI to automatically create maintenance logs from machine histories [4]. With Dürr EcoScreen MX, AI analyses of contamination patterns reduce cleaning cycles by 18% [2][4].
"Think about how you can use your data: Machine data offers you as a manufacturer and your customers enormous potential to improve workflows and processes. There is potential for new services!"
5. Energy consumption monitoring
IoT systems are changing the way energy is monitored in industrial production. With intelligent sensors and AI-supported analyses (see Trend 1), energy patterns can be recorded in real time and used more efficiently. Bosch is already using connected power meters that accurately track power consumption down to component level [6]. These technologies are based on the extended sensor networks described in Trend 3.
A good example is Sensosurf: wireless current sensors on CNC machines have reduced idle consumption by 22%. This is achieved through automated shutdown protocols [2][8]. The combination with AI-supported forecasting models, as also mentioned in Trend 1, shows impressive results in predictive maintenance platforms.
| Energy savings through IoT | Average savings |
|---|---|
| Reduction of power peaks | 20% lower peak loads |
| Idle reduction | 22% less standby consumption |
ForgeRock’s edge computing solution (see Trend 2) also plays an important role. It enables real-time load balancing in just 15 milliseconds, which is particularly important in the event of network instability [3][8].
Retrofit solutions also offer an opportunity to make older machines more efficient. This technology, which serves as a transition to Trend 7, can reduce the energy consumption of such machines by 12% on average [8]. Retrofit solutions thus create the basis for future IoT upgrades for older systems.
6. Security-first IoT design
With the increasing networking of machines, security in the IoT area is becoming more and more of a focus. A recent survey shows that 78% of German industrial companies consider IT security to be the biggest challenge in IoT projects [11]. This illustrates why security-by-design is indispensable for IoT-based maintenance models. Without robust security concepts, autonomous maintenance systems (see Trend 4) can hardly be scaled.
One example of this is Ziehl-Abegg‘s security gateway, which scans 12,000 fans in real time and reduces malware incidents by 68% [6]. The solution combines hardware security chips with role-based access (OAuth 2.0) to protect the sensor networks mentioned in Trend 3 in particular from manipulation.
| Security level | Technology | Protective function |
|---|---|---|
| Hardware security chips | Hardware security chips (TPM/HSM) | Protection against manipulation of the devices |
| Communication | TLS 1.3 + VPN | Encrypted data transmission |
| Access control | OAuth 2.0 | Role-based authorizations |
Bosch Rexroth uses TPM modules in its edge gateways, which also pre-process data for AI analyses (Trend 1). Since 2024, DIN SPEC 91471 has defined minimum standards for encrypted machine communication [11][4].
Franka Emika also uses advanced security approaches: Her industrial robots use real-time behavioral analytics that detect threats 63% faster than traditional signature-based systems [3]. This technology is based on the AI pattern recognition systems from Trend 1.
“Every IoT node requires protection from hardware level – especially with retrofit solutions (see Trend 7).” – Vincent Rüsike, Senior Consultant at TIQ Solutions [11]
Blockchain-based firmware updates reduce error rates for mass installations to less than 2% [2]. Especially for older machines (see Trend 7), retrofitting encryption modules is becoming increasingly important to ensure security standards.
7. IoT updates for older machines
As 85% of the machines are more than five years old [8]retrofit solutions offer a cost-effective way of equipping existing systems with modern functions for just 15-30% of the cost of a new machine. For example, Sensosurf’s force sensors increase system utilization by 20 % [2]while Ziehl-Abegg’s cloud-based maintenance reduces service costs by 30% [6]. These solutions rely on the safety principles from Trend 6 to safely integrate older machines into networked systems.
Similar to the AI protocol translators from Trend 1, these approaches facilitate the integration of so-called legacy systems. The retrofit process follows a fixed sequence: testing → sensor selection → edge integration → cloud connection → training [8].
| Component | Function | Improvement |
|---|---|---|
| Energy harvesting sensors | Condition monitoring | 35-50% fewer failures [2] |
| Edge gateway | Data pre-processing | 25 % higher maintenance efficiency [8] |
| AI protocol translator | Legacy system integration | 40-60% fewer warranty cases [6] |
“Start with machines that promise the greatest efficiency gains and rely on modular architectures for better scalability.” – TI Solutions [11]
Digital twins help to carry out simulations before installation [8]. Encrypted gateways ensure a secure connection between new and existing systems.
Comparison of maintenance methods
A look at traditional and IoT-supported maintenance approaches shows clear differences in efficiency and costs. This analysis is based on the IoT trends described above, in particular energy monitoring (trend 5) and retrofit solutions (trend 7). Reactive maintenance, as it is traditionally carried out, causes an average of €260 per hour in production downtime [9]. In contrast, IoT-based systems reduce maintenance costs by 30-50% [1].
| Aspect | Traditional maintenance | IoT-supported maintenance |
|---|---|---|
| Machine availability | 85-90 % | > 95 % [2] |
| Data usage | On a sample basis | Complete [8] |
| Energy saving | Hardly present | 8-12 % (based on trend 5) [6] |
| Service life of the systems | Standard | +17 % longer [11] |
Some impressive figures: PTC customers save €250,000 per year per production line, thanks to 30% less downtime [1]. Automotive manufacturers even benefit from savings of €580,000 thanks to predictive monitoring of stock levels [2].
“The digital transformation of maintenance is not a question of if, but how. Our analyses show that continuous ML evaluation reveals an average of 14 hidden inefficiencies per month.” – TIQsolutions [11]
The use of security-by-design approaches (see Trend 6) can prevent 62% of all system-related accidents [1]. At the same time, edge computing architectures (trend 2) enable 80% of data to be processed locally [5]which effectively addresses cybersecurity concerns.
The transformation is taking place step by step. Digital Twins improve decision-making accuracy by 40% [13]while edge architectures continue to process large amounts of data locally [5].
Conclusion on IoT trends in machine maintenance
The seven IoT trends provide a clear orientation for the digital transformation in machine maintenance. To implement these trends effectively, a three-stage approach is recommended:
| Implementation phases of the IoT trends | Measures | Expected benefits |
|---|---|---|
| The basics | Installation of IoT gateways, retrofitting of sensors | Reduction of manual interventions by 75% [9] |
| Optimization | Centralized data management, training of AI models | Reduction in troubleshooting time by 83% [9] |
| integration | Full automation, integration of safety solutions | Spare parts optimization by 30 [10] |
An example from Trend 4 shows that maintenance-as-a-service (WaaS) is a cost-effective way for medium-sized companies in particular to get started. The investment in IoT maintenance systems usually pays for itself within 12-18 months and generates annual savings of €150,000 on average per production line [6].
“The digital transformation of maintenance requires a hybrid approach: mechanical expertise must merge with data analysis skills. This is the only way to effectively interpret ML-generated failure predictions.” – TIQsolutions [4]
Success depends on teams that have both mechanical knowledge and data analysis skills. Security solutions such as TPM chips and end-to-end encryption (see Trend 6) also ensure the secure operation of networked maintenance systems [5].
IoT-based approaches not only make machine maintenance more efficient, but also conserve resources. The future is digital, data-driven and more sustainable.
FAQs
How will IoT develop in the next 5 to 10 years?
The future of IoT in machine maintenance will be shaped by three main factors:
| area | Forecast until 2030 | Application |
|---|---|---|
| Connectivity | Over 40 billion connected devices [13] | Real-time monitoring through 5G networks |
| AI integration | 92 % prediction accuracy [1] | Automatic error detection |
| Sustainability | 18-25% less material waste [6] | Resource-optimized maintenance cycles |
5G networks enable more precise real-time monitoring and strengthen edge computing architectures. At the same time, optimized maintenance cycles ensure less material consumption, which further improves energy efficiency.
Self-healing systems are an exciting topic. The first prototypes use intelligent materials that are activated by IoT signals [13]. From 2027, these could significantly shorten maintenance intervals and further develop autonomous maintenance systems.
From 2025, the EU Machinery Regulation 2023 will require IoT-based maintenance functions. This will make retrofit solutions for existing machines indispensable. Siemens is already working on controllers that process vibration data and meet these new standards [1][2].
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