What’s the future for the electronics instrumentation sector?

11/12/2015

Looking back at the past 10-15 years of the electronic instrumentation industry, it is certainly disappointing to realize that the market for new test equipment in 2015 is about the same size or less. What does this tell us and will the industry perform better in the future?

Recently, Frost & Sullivan published three market insights about the future of the electronic industry and what will determine it, where the new opportunities for growth are, and how to stay profitable in changing economical environment.

These market insights are listed below:

Jessy_Cavazos

Jessy Cavazos – Frost & Sullivan

“In the past decade, the electronics instrumentation industry did not maximize the revenue opportunity coming from the move towards connectivity and the proliferation of electronics as most companies missed out on dramatic changes happening in the customer base,” says Jessy Cavazos, Industry Director for Test & Measurement, Frost & Sullivan.

Over the next 5-10 years, 5G and other technologies will take the electronics instrumentation market to higher frequencies spelling significant growth opportunities for test manufacturers. The move towards a more connected, zero-latency, and autonomous world will certainly provide room for growth for the electronic instrumentation market. With the Internet of Things (IoT), a myriad of devices will be connected to the Internet. While low latency will not be provided for all applications and devices in the short term due to costs, the desire for low or no latency for a number of devices and applications is here and will provide opportunities to test manufacturers.

While wireless communications and aerospace and defense will remain significant end-user segments for electronic test and measurement equipment, demand is likely to increase in smaller end-user segments such as automotive and industrial electronics due to the greater integration of wireless technology in various devices.

The world is also on the path to become more autonomous with mobile robots, drones, and autonomous cars. While all of these technologies will translate into demand for electronic instrumentation, some, such as the autonomous car, will generate significant opportunities for test manufacturers due to the onus put on safety. Leading automotive OEMs are currently embracing automated driving translating into significant R&D opportunities for test manufacturers.

The hyper connectivity of customers will also call for a greater focus from test manufacturers on their go-to-market channels. While online channels have grown in importance for mid and low-end test equipment, this trend is also relevant to more high-end expensive test equipment from a digital marketing perspective.

“The next decade will not come without challenges for the electronics instrumentation industry. However, trends are favorable to the future growth of the electronic test and measurement market. Test manufacturers must not only be aware of the evolution of technologies and related test requirements but also expand their horizons to understand the impact of other trends on their business,” summarised Ms. Cavazos.


Applications requiring multiple smultaneous signals drive demand for advanced signal generators.

10/11/2015

The increasing sophistication of consumer electronics, rising acceptance of 4G, and the constant introduction of innovative products all contribute to the growth of the signal generator market. Signal generators have evolved from mere continuous wave devices to advanced modulation devices with superior software control, modulation capabilities and user interfaces. These improvements, along with the use of new software techniques that enhance the linearity, bandwidth and signal creation capabilities, are stoking the market for signal generators.

FS_Graphic_360Degree_Curved_400New analysis from Frost & Sullivan, Analysis of Opportunities for Signal Generators Market , finds that the market earned revenues of €693 million ($742.0 million) in 2014 and estimates this to reach €1043 million ($1127.5 million) in 2020. The study covers the segments of radio frequency (RF) tests, microwave tests, arbitrary waveform generators (AWG) and peripheral component interconnect (PCI) eXtensions for instrumentation (PXI).

Earlier, users found it challenging to synchronise multiple instruments for multichannel applications because of the closed architectural designs. This is now a thing of the past as these integrated systems share internal local oscillators.

“This will enable the synchronisation of multiple instruments and in turn, ease the tasks of test engineers.” said Frost & Sullivan Measurement & Instrumentation Industry Analyst Prathima Bommakanti. “This is important as more applications are requiring multiple simultaneous signals.”

Another important technological issue in the microwave signal generators market is the management of phase noise. Phase noise increases with carrier frequency multiplication during the generation of higher frequencies. The use of yttrium iron garnet (YIG)-based microwave oscillators, rather than voltage controlled oscillators (VCOs), is expected to help achieve the desired level of phase noise performance.

In spite of their improved functionalities, signal generators’ prices have remained stable. Since alternative integrated test solutions and have become attractive options, one way to improve the revenue generation potential of the equipment is to offer modular options.

“Developments in semiconductors, including processors, field-programmable gate array and data converters, have resulted in cutting-edge modular solutions,” observed Bommakanti. “With the communication industry introducing new standards constantly, there is a need for scalable/flexible solutions, which in turn is driving the need for PXI-based instruments, including signal generators.”


Self-Organizing Networks, Cloud-Based Radio Access Networks & IoT Drive Network Testing and Monitoring Equipment Market

26/10/2015

Network operators require analytics solutions that are not only predictive but can also test automated network infrastructure, finds Frost & Sullivan

The widespread adoption of machine-to-machine (M2M) communication, shift from reactive to predictive analytics for the Internet of Things (IoT), and continuing virtualization of network functions are compelling service providers to seek advanced testing solutions for big data and cloud analytics. Testing methodologies that can check the conformance of higher level infrastructure will prove critical in a digital environment that is characterised by long-term evolution (LTE), heterogeneous networks (HetNets) and cloud computing.

EU_PR_JNikishkina_MB0B_13Oct15New analysis from Frost & Sullivan, Global Big Data and Cloud Analytics Test Service Market and Monitoring Equipment Market, finds that the market earned revenues of €522.5 million (US $650.1 m) in 2014 and estimates this to reach €1.46 billion (US$1.63b) by 2019. The market consists of testing participants that aid in the overall visibility of the network as well as probe-based network infrastructure testing and service assurance, which aids in the monitoring of network metrics that will be collected for data analytics.

As M2M communications enabled by IoT becomes ubiquitous across industries, the copious amounts of digital data have begun to strain the networks. The issue is exacerbated by the deployment of self-organising networks (SON) and cloud radio access networks (C-RAN) technologies.

“To reduce churn in the price-sensitive telecommunication and service providers space, network operators need to actively offer exceptional quality of experience and service,” said Frost & Sullivan Measurement & Instrumentation Research Analyst Rohan Joy Thomas. “They can no longer afford to rely on traditional analytics solutions; innovative solutions that can aggregate relevant information from heaps of data in a smaller window, as well as make forecasts by visualising patterns among end users, are becoming vital.”

However, these end users continue to be sceptical about adopting big data analysis due to the market shortage of talent and skillsets. Poor technical expertise of the product could lead to serious ramifications from a security perspective.

Furthermore, end users are reluctant to deploy big data analytics due to the complexities inherent in migrating from traditional data analytics to more contemporary and innovative forms of big data analytics, particularly in more well established organizations. The siloed approach of assigning tasks to specific teams based upon the nature of work, coupled with the multi-vendor nature of network infrastructure, often challenge testing specialists. It is difficult to efficiently deploy any analytics across networks with such variable characteristics.

The scepticism associated with adopting big data and cloud analytics test services is expected to gradually abate as the CAPEX and OPEX benefits for SON and C-RAN become evident. Many telecommunications and service providers have already started restructuring their IT staff in order to provide a more holistic view into the network infrastructure.

“Industry vendors should fill the gaps in their product portfolio to facilitate a more open testing environment for their end users,” observed Thomas. “This can be achieved through partnerships with participants from other niches of the industry, as well as the strategic acquisition of market participants.”

Global Big Data and Cloud Analytics Test Service Market and Monitoring Equipment Market is part of the Test & Measurement Growth Partnership Service program. Frost & Sullivan’s related studies include: Analysis of the Global Self-Organizing Network (SON) Testing and Monitoring Equipment Market, Big Data: Implications for T&M, Global VoLTE Testing and Monitoring Market, Cloud Infrastructure Testing and Cloud-based Application Performance Monitoring Market, Global Application Performance Monitoring and Application Aware Network Performance Monitoring Market, and Global Internet of Things (IoT) Testing and Monitoring Equipment Market, among others. All studies included in subscriptions provide detailed market opportunities and industry trends evaluated following extensive interviews with market participants.


Big data analytics poised to change maintenance services models.

30/08/2015
The landscape of business opportunities in the manufacturing services sector is expected to increase 1.5 times by the end of 2020

The advent of the Internet of Industrial Things (IoIT) has triggered an influx of technology-oriented services such as cybersecurity and advanced maintenance. This has dramatically widened business opportunities in the manufacturing services sector. As integration with information and communication technologies (ICT) such as big data analytics and cloud-based platforms will form the crux of next-generation manufacturing services, solution providers are developing a portfolio of services that address security and operational improvement as well as maintenance and support.

EU_PR_JNikishkina_MB1C-10_11Aug15Services 2.0: The New Business Frontier for Profitability is part of the Industrial Automation & Process Control Growth Partnership Service program. As part of the IoIT research portfolio from the industrial automation and process control practice, this study offers a detailed assessment of key manufacturing service opportunities from an application, technology and market stand-point. The study strategically examines the transition of service models and explores the different applications of IoIT technologies, including niche segments such as plant, industrial data, security and asset/process optimisation.

New analysis from Frost & Sullivan, Services 2.0: The New Business Frontier for Profitability, finds that the paradigm of service strategies will shift from corrective to preventive and predictive maintenance services over the next five years.Effective utilisation of predictive analytics can optimise costs and eliminate unplanned downtime, which are highly attractive benefits for manufacturers. They offer complimentary access to more information on this research.

Big data analytics is poised to change the maintenance services models across the manufacturing sector. The investments for establishment of robust maintenance and support service model by leveraging the big data analytic concepts is the critical factor for the high growth rate (CAGR 9.1%, 2014-2021). “In line with the emerging trend of IoIT, manufacturing services are also evolving into a connected ecosystem supported by a single control centre,” said Frost & Sullivan Industrial Automation and Process Control Senior Research Analyst Srikanth Shivaswamy. “The demand for interoperability and maximum transparency across multiple products and processes is lending credence to the concept of connected operations.”

Such extensive integration will entail high costs for manufacturing units. The convergence of ICT with conventional services will require sophisticated platforms, further raising initial capital expenditure. However, the deployment of advanced process controls and smart communication systems will boost efficiency and compensate for the steep investments.

Strengthening cyber security infrastructure, a recent addition to the framework of industrial services, will be vital for the uptake of IoIT-based modules. Innovations in investigation, threat detection and self-aware platforms will be critical.

“Overall, solution providers will be rated on one of two factors,” stated Shivaswamy. “Customisation of service models to match the needs of end users or the capability to migrate to a different service model in alignment with a new end-user process, product or solution.”

Delivering these competencies will allow services providers to mine lucrative prospects in under penetrated resource-based production industries. The availability of cost-effective solutions will lure small- and medium-scale manufacturers to implement IoIT-based systems, thus completing the shift from traditional to managed services.


Innovative biosensors incite use in non-traditional applications.

07/08/2015

Besides healthcare and food, biosensor devices are penetrating the mobile, security and automotive segments, notes Frost & Sullivan

Click image  for complimentary access to more information on this research.

Click image  for complimentary access to more information on this research.

The biosensors market is proving highly attractive as it exhibits continuous growth in applications, penetration into newer sectors, and development of devices resulting in higher revenue year after year. The global biosensors space has seen the entry of multiple participants each year with none having exited the market so far.

Recent analysis from Frost & Sullivan, Analysis of the Global Biosensors Market, finds that the market generated revenues of $11.53 (€10.54) billion in 2014 which is estimated to more than double to $28.78 (€26.31) billion in 2021. Though innovation has facilitated biosensor penetration into a number of diverse markets, healthcare and food pathogen detection are currently the largest application segments.

“With health and wellness becoming a priority for all concerned in the value chain – individuals, governments, healthcare institutions, diagnostic device developers, system integrators, the medical fraternity and insurance companies – biosensors are acquiring more importance,” said Frost & Sullivan Measurement & Instrumentation Industry Principal Dr. Rajender Thusu. “For instance, strict food safety regulations enacted by federal governments to improve the health of consumers, require the use of biosensors for compliance monitoring.”

Under these regulations, meats, milk and milk products must be tested for the absence of a number of pathogens before being processed and supplied for consumption. Along with the rising trend of testing fresh vegetables and processed food for the presence of different pathogens, these norms are fuelling the adoption of testing kits.

Significantly, the use of biosensors is expanding to diverse end-user markets. While security agencies are using biosensors to detect drugs, banned substances and explosives, biosensors are also a valuable tool for monitoring health of soldiers.

Realizing the benefits, biosensor manufacturers have started to move to mobile platforms which will enable users to monitor key health parameters in real-time. Biosensor relevance in automotive applications will grow with the use of cognitive biosensors to boost driver alertness and enable safety.

Manufacturers must strive harder to meet the stringent and specific requirements of a number of industries such as wearable medical devices, food processing, environmental, bio-defense, and automotive.

Biosensor manufacturers must also look into other issues such as the long detection times associated with existing test methods in some applications. As samples need to be enriched in some cases before one can test for the presence of pathogens.

“Several companies are investing in R&D to innovate and improve biosensor technology, make it highly sensitive, and develop technology platforms to reduce detection time appreciably,” noted Dr. Thusu. “Since the long development cycle of biosensor devices is another challenge, manufacturers are trying to address this by deploying both optical and non-optical technologies.Rapid detection biosensor devices are the need of the hour for a number of applications.”

Further, manufacturers are developing nano-biosensors, with features to detect pathogens at a concentration as low as one cell per five milliliters of water. Advanced-stage research is also being conducted to create unique biosensors that can detect cell-to-cell interactions in therapeutic monitoring.


Demand for IoT testing and monitoring equipment.

28/06/2015

As the trend towards connected living and the Internet of Things (IoT) continues to permeate home, work and city solutions, the need to keep tabs on a myriad of connected devices will thrust the global IoT testing and monitoring equipment market into the spotlight. The incorporation of machine-to-machine (M2M) communication – central to IoT deployment – as well as modules that require less power and bandwidth will bring with it several challenges that turn into a boon for testing and monitoring vendors.

New analysis from Frost & Sullivan, Global fands Equipment Market, finds that the market earned revenues of $346.9 million in 2014 and estimates this to reach $900.1 million in 2021.

“As the escalating number of connected devices adds breadth to the IoT concept, solutions that can proactively monitor, test and zero in on anomalies in the infrastructure will garner a sustained customer base,” said Frost & Sullivan Measurement and Instrumentation Research Analyst Rohan Joy Thomas. “The incorporation of new testing and wireless standards will broaden testing requirements and further aid development in IoT testing and monitoring equipment.”

Educating end users on the importance of interoperability and the requirement for specialised testing equipment is vital for market success. Currently, the lack of end-user awareness on the need for proactive solutions stalls the large-scale use of IoT testing and monitoring equipment. End-user inability to identify the most appropriate solution from a plethora of identical systems too limits adoption.

High capital expenditure associated with procuring equipment coupled with inadequate standardisation around IoT adds to the challenge. Such concerns over high investment costs and standardisation should abate as IoT matures in the years ahead.

“Industry vendors must fill the gaps in their product portfolio in order to facilitate an open testing environment and lay the foundation for long-term growth,” concluded Thomas. “To that end, building partnerships with or acquiring participants from other industry niches will help solution providers extend their horizons in the global IoT testing and monitoring equipment market.”


High-Fidelity battery modeling.

26/05/2015
The use of virtual battery technology in the design of test systems can facilitate the development of better products, reduce project risks, and get products to market faster.

The use of rechargeable batteries in consumer products, business applications and industrial systems continues to grow substantially. The global market for all batteries will reach almost €68 billion (US$74b) this year, and rechargeable batteries will account for nearly 82% of that, or €55 billion (US$60b), according to market researcher Frost & Sullivan.

Figure 1: Simulation of thermal runaway using the Li-Ion model from the MapleSim Battery Library

Growth like this means several things. First, large companies have moved or are moving into the market, designing and offering products ranging from hand-held devices to large power back-up systems. Second, as the systems get larger, battery technologies have to match the technical challenges of increasing cell capacity, thermal stability, life extension and disposal.

Meeting the Technical Challenges

Monitoring and controlling larger cell arrays through Battery Management Systems (BMS) helps to minimize charge times and maximize efficiency and battery life. Design and testing of a sophisticated BMS can pose challenges, however, as was discovered by one of the largest producers of electronic products in the world. That’s why they recently relied upon Maplesoft and ControlWorks Inc., a real-time testing systems integrator with deep experience developing BMS test stands, to develop a Hardware-in-the-Loop (HIL) test system for the BMS in one of their large  Energy Storage System (ESS) products.

An attractive solution to these testing challenges is to use virtual batteries – mathematical models of battery cells that are capable of displaying the same dynamic behavior as real ones – for early-stage testing of the BMS. Not only have these models proven to be highly accurate, they are computationally efficient and are able to achieve the execution required to deliver real-time performance for batteries containing hundreds of cells on real-time platforms.

The battery modeling technique employed by Maplesoft uses a partial differential equation (PDE) discretization technique to streamline the model to a set of ordinary differential equations (ODE) that can be readily solved by system-level tools like MapleSim. The advanced model optimization features of MapleSim also allow the resulting code to be very fast and capable of running in real-time.

The resulting battery models can also be employed in the prediction of charge/discharge rates, state of charge (SoC), heat generation and state of health (SoH) through a wide range of loading cycles within complex, multi-domain system models. This approach provides the performance needed for system-level studies with minimal loss in model fidelity. The user can also allow for energy loss through heat, making these models useful for performing thermal studies to determine component sizes in cooling systems to manage battery temperature. Not carefully controlling the temperature can lead to reduced operational life or, in extreme cases, destruction or even explosion due to thermal runaway, a common problem in many battery-powered systems.

Model Structure for this Application
For the purpose of this ESS test system development project, the key requirements for the battery model were:

-Up to 144 Li-Ion polymer cells for testing the BMS of the client’s ESS products
-Ease of configuration for different requirements (parallel/series networks)
-Several sensors per cell (current, voltage, SoC, SoH)
-Variation of chemistry make-up due to manufacturing tolerances
-Fault-insertion on each cell (open-circuit, shorting)
-Capacity to run in real-time (target execution-time budget of 1 ms)
-In the case of energy storage systems, like this example, each ESS battery is made of several “stacks” that, in turn, contain several cells. The MapleSim model follows this structure with each cell being a shared, fully parameterized subsystem. Each cell can also be switched to open circuit using logical parameters.

Figure 2: Cell stack model

The stack model is made of 18 cell subsystems connected either in parallel or series, depending on the requirement. Input signals are provided for charge balancing from the BMS. Output signals are provided back to the BMS to monitor the condition of the stack (supply voltage, SoC and SoH). Finally, the full ESS is made of several stacks with IO signals fed to and from the BMS.

Figure 3: ESS Battery model

Model Calibration and Validation
Much of the accuracy of this model is dependent on experimentally derived parameters, determined from charge/discharge test results. Project engineers determined that any deviation in performance due to manufacturing variations needed to be included in order to test the charge-balancing capability of the BMS. Instead of testing every cell, engineers relied on random variants generated from the statistical distribution determined by the charge/discharge test results on 48 cells. This was applied to all 144 cells and then compared with the real test results. The maximum variance of the voltage from the experimental data was 14mV, while from the simulation it was 13mV, acceptable for the purpose of this project.
Maplesoft and ControlWorks Inc. engineers also determined the average cell response using the parameter-estimation tool supplied with the MapleSim Battery Library. This uses optimization techniques to determine the values of cell-response parameters that provide the closest “fit” to the experimental results. This response was then validated against response data from other cells to ensure close estimation of the resulting model.

SoH behavior was implemented as a look-up table based on experimental results. The model determines the capacity and internal resistance based on the number of charge/discharge cycles and depth of discharge (DOD) from the lookup.

Figure 4: SoH simulation showing effect on battery voltage

Finally, the model was converted to ANSI-C through the MapleSim Connector, producing an S-Function of the battery model that can be tested for performance and accuracy with a fixed-step solver on a desktop computer in MATLAB/Simulink® before moving it to a real-time platform. The simplest solver was used and the performance bench showed that the average execution time was approximately 20 times faster than real-time, occupying 5.5% of the real-time system time budget. This shows that the battery model can be easily scaled up, if required.

The end result was a battery model capable of being configured to represent a stack of up to 144 cells that can be connected in any combination of parallel and series networks. Fault modes were also built-in, such as individual cells shorting or opening, as well as incorporating variations in charge capacity from cell to cell, and degradation of capacity over the life of the cells.

The final BMS test station provides the client’s engineers with the ability to configure the battery model (number of cells, series/parallel, etc.) and apply a range of tests to it. The engineer can go back to the MapleSim™ model at any time to make any necessary changes to the model configuration, and then generate the model for use on the real-time platform. In this system, the real-time software is National Instruments’ VeriStand™, driving a PXI real-time system. The MapleSim Connector for NI VeriStand™ automates the model integration process, allowing the engineer to produce the real-time model quickly and reliably.

The ControlWorks Inc. system also integrates real-time platform, signal processing, fault-insertion tools and standard communications protocols (CANbus for automotive, Modbus for industrial applications), allowing the engineer to run the BMS through a range of tests on the battery model, including Constant Current (CC) and Constant Voltage (CC/CV) charge/discharge cycles, as well as Constant Power (CP) and Constant Resistance (CR) discharge cycles.

“We were pleased to be able to partner with Maplesoft on this project,” said Kenny Lee, PhD, Director of Research Center of Automotive Electronics, ControlWorks Inc. “The use of battery models in this case proved to be an effective alternative to the use of real batteries,” he added.

Summary
Test automation and simulation is critical in system-level testing, allowing time and cost of failure analysis, constant development pressure, expense of repeated tests, and lengthy set-up times all to be addressed.

“The use of high-fidelity, ready-made battery models allows the engineer to avoid the risks of damage to batteries, along with subsequent costs, while testing and optimizing the BMS design in a close-to-reality loading environment,” said Paul Goossens, Maplesoft VP of Engineering Solutions.
The use of virtual battery technology in the design of test systems can facilitate the development of better products, reduce project risks, and get products to market faster.

“The MapleSim model of the Li-Ion battery was selected because of its proven ability to achieve real-time performance. The code-generation and compilation tools are very easy to use, making the integration of the model into the HIL system very fast and cost-effective. That, plus the excellent development support we received from Maplesoft’s Engineering Solutions team made this a very smooth project.”  Kenny Lee, PhD, Director of Research Center of Automotive Electronics, ControlWorks Inc.