How to Achieve Operational Excellence in Wind Farms


The global renewable energy industry has seen unprecedented growth over the last several years. According to the Global Wind Energy Council (GWEC), the cumulative installed capacity of wind power projects has increased from 24 GW in 2001 to 432 GW in 2015 and is expected to grow to 703 GW by 2020 (Global Data). Much of this growth has resulted from great state and national level policies, tax incentives and high electricity prices. However, many of these incentives have since been reduced and now wind power companies face pressure to improve profitability, while scaling operational excellence. Mechanical engineering and physics based improvements have long been used to increase plant operations. However, these improvements plateau after a period of time thus providing diminishing returns. In the increasingly connected world of today, sensors are being used to achieve operation excellence based on real time, plant specific data. Companies now are realizing the value of using data to maximize returns as compared to making any physic improvements to the components. In this day and age of the Internet of Things (IoT), sensors are being used to record data from every device, and the renewable energy industry is not one to be left behind.

Governments around the world are slowly scaling down renewable energy incentives such as tax benefits and generation based incentives while renewable energy electricity prices are falling. Companies are thus focusing on minimizing operations and maintenance (O&M) costs and maximizing power production. Data analytics enables O&M teams to achieve both goals by taking a closer look at the data from every plant device. According to a recent report by McKinsey, depending upon the plant’s existing level of performance, better O&M could account for as much as a 20% increase in IRR.  The type of data analytics for better wind farm O&M falls into three categories: a) forecasting, b) alarms based on threshold values, and c) condition monitoring. Continue reading

Cortex Agent, The Edge-Computing Middleware

Cortex Agent, The Edge-Computing Middleware

Cortex Agent, The Edge-Computing Middleware

MachinePulse introduces a powerful real time IoT middleware software for Industrial Internet Edge computing

MachinePulseTM has announced their recent launch of Cortex Agent, an Internet of Things (IoT) middleware that seamlessly integrates devices on Industrial Internet offering a complete suite of features for end-to-end management of various types of devices and applications for any Cloud Platform or centralised infrastructure.

Cortex Agent is designed to aggregate the data silos which otherwise remain isolated because of incompatibility, legacy protocols and different sub networks. This multi featured middleware addresses this complexity. In addition to its edge computing capabilities, its features makes it infinitely scalable (through tree topology) and can connect to thousands of data source endpoints in real-time. It supports multiple databases and messaging platforms.

“From implementing multithreaded, high performance industrial grade integration of multiple different devices and applications with queuing to performing critical functions like aggregating, filtering, standardisation, scaling, enumeration of complex expressions, historization, computing the information, performance benchmarking, granting access control to the devices and streaming the data to cloud platforms, Cortex Agent is the outcome of 50,000 man-hours devoted by our commendable team “, said Basant Jain, CEO of MachinePulseTM.

Furthermore, Cortex Agent addresses the security and privacy concerns plaguing the industry due to the exponential growth of the amount of data generated by the Internet of Things. It provides secure peer-to-peer communication and secure authentication of new peers, permissions to access the network and protection of information exchanged in the network. Cortex Agent also  offers an Application Program Interface (APIs) that allows users to build custom application without going through complicated device protocol implementations and integrations.

A powerful middleware on its own, Cortex Agent with its robustness, reasonable cost and standard based approach to solve modern industrial internet problems, assists impeccably in the integration of Cyber Physical Systems. Cortex Agent is now available for enterprise and OEMs for integration and  usage.

About MachinePulse

MachinePulseTM is an Internet of Things platform provider based in Mumbai. The big data platform, erixisTM, has in-built machine learning and predictive analytics capabilities. MachinePulseTM has branch offices in Chennai and Bangalore. To learn more, email or follow them on

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MachinePulse in collaboration with Indian School of Business (ISB)


MachinePulse is collaborating with ISB (Indian School of Business) and partaking in the Capstone Project 2014-2015, conducting students and candidates towards a culminating academic and intellectual grounding at the end of their learning-pathway experience. Candidates are working on various real-time projects in either Solar, Wind and Factory domains pivoting around mechanisms that prognosticate failures and autonomously prompt maintenance procedures or respond to unanticipated changes in the plants, delivering high-end quality service with minimum overhead expense using various methods and algorithms to specifically suit the requirements of the industry. Candidates are also helping the company to create tool/framework to help them understand the parameters which affect  power fluctuations and also determine maximum threshold for grid safety.

For further information about ISB, please visit their website.

About MachinePulse:

MachinePulse is a provider of rapidly scalable solutions addressing the machine data requirements of industrial protocols, M2M and IoT segments with real time big data analytics and decision science on a cloud platform. We provide machine data solutions powered by erixis™, our intelligent cloud based, analytics platform. Contact us to learn more.


Monetization of Advanced Metering Infrastructures (AMI) with Big Data Approach

Indian electricity utility companies have long been under scrutiny for their poor management of energy consumption and assets. Energy thefts, rising green house gas emissions and poor match with demand and supply of energy are some of the struggles of using traditional energy meters. The answer to this point at issue is advanced metering infrastructures (AMI) facilitated with Big Data Analytics which can impart granular insights into  customer usage motif.

Smart grids are  conducive to superior electricity delivery infrastructures. So, utilities are attempting to pursue high volume data management to harvest dynamic behavioral patterns in the physical grid. Blending and crunching variegated data sets  can yield more insights and knowledge that will need to be acted upon. But, analyzing this amassed wealth of information is an extensive trial. Analytics and Big Data are intrinsically linked to each other as application and foundation layers and reinforces the monetization of assets.

There are three domains infused in Smart Grid Utilities Analytics:

  • Enterprise Analytics- Grid Visualization and Business Intelligence equipped with Predictive Analytics
  • Grid Operations Analytics- Optimization and Operational Intelligence, Management of assets and outage, Fault Detection and Correction
  • Consumer Analytics- Behavioral analytics and Load Flow, Data Integration on social media

A convenient advantage of using AMI is that it enables bidirectional communication between the consumer and utilities. From a consumer perspective, apart from management of energy consumption and outage using AMI, reduction of energy bills and greenhouse gas emissions is an ancillary benefit. With more control and power over the  amount of energy consumption with the assistance of Big Data Analytics, consumers and utilities alike will be adept in keeping track of energy consumption, load, ROI, revenue losses, energy T&D, service levels and outage, improving SLA ( service-level-agreement). India is expected to install 130 million smart meters by 2021.

Data acquisition is not the challenge. The struggle to keep the management staff informed of their analysis fast enough is the critical problem. The ability to enable operational decisions through transformation of data into actionable intelligence and feeding them to the consumers is not possible if the rate of analysis is low. Execution of data analysis is not pronounced in utilities due to  lack of skill to visualize, analyze and comprehend.

If we look into the Indian Smart Grid, the major hurdle is the availability of funds. To tackle this financial challenge, compelling smart grid consumer products, vendor partnerships and a willing investment body seems like the answer. Energy thefts and commercial losses, common in the  country unlike developed nations can be evaded by use of tamper-proof smart meters with a prepaid system.  But, for operational success, AMI will require faultless connectivity and India is not at par, in this regard, with other developed nations to employ a communication of the extent required for Smart Grids. The capital and hardware costs escalates if investment is done on an excellent communication network setup. This may impact during service deliveries in a negative manner. Due to the lack of awareness, utilities in India need to shift their focus to educating the consumers of the overall potential of Smart Grids rather than mere adoption of smart meters. Now, the benefits from the Smart Grids may be favorable. But, when mismatched policies and incentives come into the picture, the investment seems less attractive.  The risk factor associated with utilizing Smart Grids is shared by every individual in the value chain. Maintaining  this, will help align the incentives, tackle policy disputes and offer promising solutions.

If all the  above mentioned challenges are catered to, the utilization of Smart Grids and Big Data  in the country will not only accentuate sustainable use but also, monetize AMI data. MachinePulse, a Mumbai based IoT platform provider is soon, looking forward to incorporating smart grid data analytics and solutions and transform the smart meter data silos into actionable insights.

About MachinePulse

MachinePulseTM is an Internet of Things platform provider based in Mumbai. The big data platform, erixisTM, has in-built machine learning and predictive analytics capabilities. MachinePulseTM has branch offices in Chennai and Bangalore. Email us to learn more.

6 challenges predictive analytics

6 Challenges To Adopting Predictive Analytics for the Industrial Internet

Predictive analytics provides a complete gamut of benefits that allow companies to transform their existing business models. For example, one of the major benefits of using predictive analytics in the Industrial Internet is the identification of performance degradation in real time and failures detection before they occur which reduces unexpected downtime and  helps overcome a plethora of associated challenges. An Industrial Internet equipped with predictive analytics capabilities delivers safer and more reliable outcomes that enable operations teams to create accurate maintenance and replacement schedules. Many industries understand the value of and use predictive analytics for lesser downtime, improved operational efficiency, etc. However,  there still exists a gap between what is possible with using predictive analytics and what is currently being done. To gain the most of predictive analytics, it is important that industries address the following six challenges before adopting it:

  • The data must be error-free and well-timed: Terabytes and petabytes of data gathered have no value for decision-making purposes if the information is not error-free and well-timed.
  • Inaccurate predictions: Hedge against the possibility of inaccurate predictions by instituting checks results.
  • Outdated analytics methods: Frequently refresh the predictive analytics models as they deteriorate over time.
  • Building analysts’ skills: Organize regular training sessions for the data scientists since data science is an evolving field and requires the scientists to be aware of frequent updates.
  • Supplement with other models: Predictive model alone sometimes is not sufficient where inaccuracy cannot be afforded.
  • Pick the right team: Predictive analytics is not widely understood yet and many data scientists or analysts lack the experience to create deep insights based on their findings.

It is crucial that industries address these challenges before adopting predictive analytics techniques.

MachinePulse understands the importance of predictive analytics in improving operational efficiency. All our solutions are equipped with predictive analytics models and we can work with your industry to address the challenges listed above.

About MachinePulse

MachinePulseTM is an Internet of Things platform provider based in Mumbai. The big data platform, erixisTM, has in-built machine learning and predictive analytics capabilities. MachinePulseTM has branch offices in Chennai and Bangalore. Email us to learn more.


SolarPulse 3.0 By MachinePulse Makes Solar Power Projects Smarter

SolarPulseTM  3.0 uses real-time analytics and artificial intelligence to offer performance insights thereby enhancing operational efficiency  of solar power plants.

MachinePulseTM, launches SolarPulseTM 3.0, an analytics solution for the solar power industry. Catering to industrial, commercial and utility sectors, SolarPulseTM 3.0 is the new and improved version of its predecessor SolarPulseTM 2.0, which was introduced in 2013.

The utility solution’s scalable (horizontally and vertically) platform allows monitoring of multiple parameters through no node locking, allowing the user to find the root cause of performance issues easily. All devices are dynamically compared against the best performing device to enable users to spot performance issues through bird eye monitoring on a real-time basis. Users can define their own rules and notifications for benchmarking to a string level granularity. The geographical plant map feature enables quick fault detection through one single dashboard for viewing device performance through a color coded status. SolarPulseTM 3.0 for rooftop applications is offered through the erixisTM cloud platform. The cloud solution offers features that equip solar plant owners with the right tools to achieve optimum performance of their power plants. It offers unification of all data on a single platform, all existing as well as upcoming roofs and an interactive platform for multi-roof comparisons and drill down. Through centralized monitoring of rooftops, geo visualizations of all sites as well as individual rooftops are made available.

SolarPulseTM 3.0 offers stunning, interactive data visualization with a  user friendly interface for configuring new devices and parameters for individual needs. Furthermore, SolarPulseTM 3.0 addresses the drawbacks of faulty predictions by using artificial intelligence based models to accurately predict performance, irradiation, maintenance issues, etc. With these predictions, users can make informed decisions based on real-life accurate data.

About MachinePulse

MachinePulseTM is an Internet of Things platform provider based in Mumbai. The big data platform, erixisTM, has in-built machine learning and predictive analytics capabilities. MachinePulse has branch offices in Chennai and Bangalore. To learn more, email


Why Factories Using Artificial Intelligence Approaches Are The Future

  • Artificial Intelligence (AI) can be advantageous, especially where the data exhibits some form of non-linearity.
  • To reproduce some of the flexibility and power of the human brain, artificial neural networks (ANN) try and emulate the brain structure through computational models.
  • Different personality types should also be taken into consideration while designing appropriate tools and solutions.

A smart factory with interlinked smart systems is powerful – it responds to customer demands and changes within a short span of time, makes better predictions of future operations, allows for more informed decisions that improve efficiency, etc. However, smart factories are yet to function independently without human involvement. Embodying accurate human-like thinking into a mechanical system will create the most efficient and optimized factories and manufacturing units.

Fuzzy Logic and Neural Network in AI

When it comes to tasks requiring delicate human intuition based decisions, Artificial Intelligence (AI) can be advantageous, especially where the data exhibits some form of non-linearity. Fuzzy logic (FL) and neural network theory (NN) are complementary constituents of AI rather than competitive as it is evident and it is beneficial to employ FL and artificial NN in combination rather than exclusively. To reproduce some of the flexibility and power of the human brain, artificial neural networks (ANN) try and emulate the brain structure through computational models. Suppose, there are five machines running in parallel performing different tasks and one of them gives out an undesired output due to some unseen problem in the machine. Ideally, an intelligent system will retrieve cases from memory that are relevant to solving it and map the solution of that case to the target problem. It will not only map the solution to the problem but, will also adapt the solution as required to fit the new case and will apply the new solution in the real world and store this solution under a new case for future use. Now, in a situation like above, if the other four machines are dependent on the output of the first machine, it might also be possible that these machines might face similar problems which might compromise the safety of the unit. To prevent this, the first system will alert the other systems regarding the faulty situation and the other systems will be able to similarly map out solutions accordingly. This is a classic example of machines adapting and displaying flexibility to different situations independently using a combination of FL and ANN. However, using anecdotal evidence as the main operating principle with no statistically relevant data to support a generalization, is not enough to prove that the generalization is accurate. It is also essential to remember that human beings are not completely rational. Different people might react differently to the same situation and make decisions based on their own unique experiences. Different personality types should also be taken into consideration while designing appropriate tools and solutions.

FL and ANN are not widely used in the manufacturing industry however, once the methods gain traction,  and familiarity, the process of configuring such a system will become relatively easy. It is clear that this technological shift in manufacturing industries is here to stay with the promise to improve the quality, safety and precision.

MachinePulse is bringing the groundbreaking results of artificial intelligence approaches to factories through FactoryPulse. The Internet of Things solution has AI capabilities that improve the overall efficiency of projects leading to better performance and higher returns.

About MachinePulse
MachinePulse™ is an Internet of Things platform provider based in Mumbai. The big data platform, erixis™, has in-built machine learning and predictive analytics capabilities. MachinePulse is based in Mumbai, India with branch offices in Chennai and Bangalore. Contact us to learn more.