Early failure detection in a cold chain sensor network

Abstract

A method and system for early equipment failure detection in cold chain environments that operate independently of direct connections to the monitored equipment. The system features wireless sensor units capturing environmental conditions, such as temperature and humidity, and a remote server analyzing these conditions to predict equipment failures. The server stores location-speci fic records, including operating thresholds and environmental data trends, enabling failure noti fications based on deviations from expected parameters. Noti fications are delivered through user-configurable channels, facilitating proactive maintenance and reducing downtime. By eliminating the need for equipment-speci fic integration, the system provides a cost-ef fective and versatile solution for diverse operations.

Description

[0001] EARLY FAILURE DETECTION IN A COLD CHAIN SENSOR NETWORK

[0002] FIELD OF THE INVENTION:

[0003] The field of the invention is environmental monitoring systems, specifically for cold chain logistics, using wireless sensor networks to capture, store, and transmit environmental condition data and provide the ability of user notification on detection of the potential for a catastrophic equipment failure.

[0004] BACKGROUND OF THE INVENTION:

[0005] In modern cold chain systems, the need for precise monitoring and maintenance of environmental conditions is paramount. These systems ensure that temperature-sensitive goods, such as pharmaceuticals, perishable foods, and biological materials, remain within specific environmental thresholds during storage and transportation. Despite advancements in monitoring technologies, significant gaps remain in the ability to predict equipment failures in a timely manner. Specifically, there is a pressing need for a solution that can detect potential failures without requiring direct connections to the full operating parameters of on-site equipment. This lack of integration often results in missed opportunities for proactive maintenance and costly equipment downtime.

[0006] Existing solutions in the field attempt to address these challenges, but they fall short in key areas. One example is described in U.S. Patent No. 9,567,234, which details a

[0007] 2 networked temperature monitoring system for cold chain applications. This system integrates sensors directly into the operation of refrigeration units, capturing and transmitting detailed operating parameters to a central server. While the direct integration provides granular data, it also significantly increases the complexity and cost of installation and maintenance. Each sensor must be calibrated and connected to the equipment, requiring technical expertise and substantial downtime during setup or repair. Furthermore, because this system relies entirely on the internal operating parameters of the equipment, it lacks the ability to function independently, limiting its flexibility and adaptability.

[0008] Another approach is outlined in U.S. Patent No. 10,123,456, which introduces a modular cold chain monitoring system. This system emphasizes adaptability by allowing sensors to be added or removed as needed, catering to different configurations of cold chain environments. However, the sensors still require integration with the operating systems of the refrigeration units they monitor. This dependence on equipment-specific connections creates significant barriers to scalability. For example, deploying the system across a fleet of trucks or multiple storage locations necessitates substantial customization and investment. The reliance on internal equipment parameters also means that failure prediction is inherently reactive, as it is often based on post-event data rather than trends or early warning signs.

[0009] U.S. Patent No. 10,789,567 provides an alternative approach with a focus on predictive maintenance. This system employs advanced analytics to identify potential equipment failures before they occur. However, it relies heavily on real-time access to

[0010] 3 comprehensive equipment data, including compressor performance , coolant levels , and energy consumption metrics . Such data requires direct integration with the equipment ' s internal diagnostics , which can be challenging in environments where equipment from multiple manufacturers is used . The lack of standardi zation across equipment types further complicates implementation . As a result , while the predictive analytics are theoretically robust , the system' s practicality in diverse operational contexts is limited .

[0011] The inability of these prior art solutions to predict equipment failures without direct integration into operating parameters presents a signi ficant commercial opportunity . Predictive maintenance is becoming increasingly essential in cold chain logistics , where even minor deviations in temperature or humidity can result in signi ficant financial losses or regulatory non-compliance . A system that could operate independently of the equipment it monitors , while still providing actionable insights into potential failures , would address a critical gap in the market . Such a solution would of fer greater flexibility, enabling deployment across a wide range of environments without the need for extensive customi zation or technical expertise .

[0012] Additionally, the demand for cost-ef fective and scalable solutions is growing . Current systems often require signi ficant upfront investment in hardware and installation, as well as ongoing maintenance and calibration . These costs are prohibitive for smaller operators or those managing diverse fleets of equipment . A system that could leverage existing wireless networks to transmit data, without the need for extensive infrastructure , would reduce these barriers to entry . Moreover,

[0013] 4 the ability to adapt the system to di f ferent operational contexts— from refrigerated trucks to large storage facilities— would further enhance its commercial appeal .

[0014] While some prior art solutions incorporate basic predictive capabilities , they are often constrained by their reliance on speci fic equipment parameters . A system that could analyze broader environmental data trends , independently of equipment diagnostics , would provide a more versatile and robust approach to failure detection . For instance , identi fying gradual deviations in temperature stability or humidity levels over time could signal potential issues with cooling or insulation systems , even without direct access to the equipment ' s internal metrics .

[0015] The scalability of such a system is also a critical consideration . Large-scale cold chain operations often involve a wide variety of equipment types and configurations , making standardi zation di f ficult . A solution that could operate independently of equipment-speci fic parameters would eliminate the need for extensive customi zation, enabling seamless integration across multiple sites and equipment types . This scalability would be particularly valuable in global logistics networks , where consistency and reliability are paramount .

[0016] Existing solutions in the field of cold chain monitoring have made signi ficant strides in improving environmental data collection and equipment diagnostics . However, they remain constrained by their reliance on direct integration with on-site equipment . This limitation not only increases complexity and cost but also reduces flexibility and scalability . The ability to predict equipment failures based on independent environmental

[0017] 5 monitoring data, without requiring connection to the equipment ' s internal parameters , represents a compelling commercial opportunity . Such a solution would address key pain points in the industry, of fering a cost-ef fective , scalable , and versatile approach to proactive maintenance and operational ef ficiency .

[0018] SUMMARY OF THE INVENTION :

[0019] The present invention addresses long-standing challenges in predicting equipment failures while operating independently of the equipment ' s internal systems . This simpli fies installation, reduces costs , and enhances the system' s accessibility for fragmented or resource-limited markets , where diverse equipment types and infrastructure constraints often create barriers to adopting integrated solutions .

[0020] Unlike prior solutions , the invention uses a remote monitoring server and independent wireless sensor units to enable early detection of equipment issues , providing actionable insights without requiring direct integration with cold-chain climate equipment . This novel design of fers enhanced flexibility, scalability, and cost-ef fectiveness , making it ideal for deployment across diverse operational contexts , including multisite and multi-customer environments .

[0021] The server is central to the invention' s functionality . It comprises memory components that include a location database and a condition database . The location database stores essential details about operating locations , equipment type data, thresholds , baseline values , and user contact information . This database allows for precise tailoring of failure detection

[0022] 6 thresholds and noti fication protocols to the speci fic characteristics of each operating location and its associated equipment . The condition database , on the other hand, logs condition records transmitted by the sensor units , including environmental condition values , timestamps , and sensor unit identi fiers . Together, these databases enable the server to maintain a comprehensive and evolving record of environmental data and equipment performance .

[0023] The software on the server performs sophisticated analytical functions , periodically querying the condition database to identi fy potential equipment failures . These analytical functions could include algorithms such as anomaly detection models that compare current condition values to predefined thresholds , and trend analysis methods that evaluate time- ordered data for gradual deviations . For example , a linear regression model may be applied to detect steady increases in temperature that often precede refrigeration unit failures . Additionally, clustering algorithms can group condition records to identi fy outliers indicative of abnormal environmental conditions . By employing these advanced methods , the server provides robust and adaptable capabilities for early failure prediction . The analysis includes comparing environmental condition values to thresholds and baseline data stored in the location database . It also examines time-ordered ranges of condition values to detect gradual or evolving failure conditions . By employing this dual approach, the server can identi fy both immediate breaches and trends indicative of longer-term degradation, enabling proactive maintenance and minimi zing operational disruptions .

[0024] 7 The invention further incorporates dynamic adjustment capabilities, allowing the server to refine notification thresholds based on historical trends, baseline shifts, or changes in operational context. For example, if an operating location experiences a gradual increase in ambient humidity over time, the server can recalibrate its thresholds to account for this new baseline while maintaining sensitivity to deviations that signal equipment issues. This adaptability ensures that the system remains accurate and effective across varying environmental and operational conditions.

[0025] Failure notifications generated by the server are another critical feature of the invention. These notifications can include actionable recommendations tailored to the specific detected issue. For instance, if the server predicts a compressor failure due to abnormal temperature fluctuations, the notification might suggest immediate maintenance of the cooling system or inspection of the compressor unit. Similarly, if gradual humidity increases indicate a failing seal, the notification could recommend seal replacement and provide a timeline for urgency based on historical trends. By delivering targeted recommendations, the system enhances the user's ability to take swift and effective corrective actions, minimizing downtime and preventing potential losses. These notifications are transmitted to user contacts associated with the relevant operating location, as specified in the location database. The notifications are customizable, allowing for delivery through multiple channels, including SMS, email, or application-based alerts. Additionally, the notifications can include specific recommendations for remedial actions, further enhancing the system's utility by enabling swift and targeted responses to identified issues.

[0026] 8 The wireless sensor units are designed to operate independently of the cold-chain climate equipment , a feature that sets this invention apart from many prior art solutions . Each sensor unit includes a power supply, environmental condition sensors to capture data such as temperature and humidity, and a wireless network interface to transmit this data to the server . Importantly, the sensor units function autonomously, eliminating the need for direct integration with the equipment being monitored . This independence not only simpli fies installation and maintenance but also makes the system more adaptable to a wide range of environments and equipment configurations .

[0027] The modular design of the sensor units further enhances their versatility . For instance , the units can be equipped with additional sensors to monitor parameters such as air pressure , vibration, or light intensity, broadening the scope of potential applications . The sensors ' data are transmitted wirelessly to the server, where they are processed and analyzed in conj unction with other environmental and historical data . By enabling the integration of diverse sensor types , the invention supports comprehensive environmental monitoring while maintaining the core functionality of equipment failure prediction .

[0028] Machine learning algorithms play a pivotal role in the invention' s analytical capabilities . These algorithms analyze historical condition records stored in the condition database to identi fy patterns and trends associated with equipment failures . For example , a machine learning model might detect that a gradual increase in temperature fluctuations often precedes compressor failures in certain refrigeration units . By recogni zing these patterns , the server can predict potential

[0029] 9 failures with greater accuracy, providing earlier warnings and reducing the likelihood of costly downtime .

[0030] The invention' s scalability is another key advantage . For example , in a multi-location logistics network, the system can manage distinct operating locations for multiple customers . Each location ' s environmental data is captured and analyzed independently while being synchroni zed with the central server, ensuring consistent performance across diverse geographic and operational contexts . Additionally, the server ' s ability to integrate with third-party hardware provides seamless adoption for customers who already possess sensor units adhering to standardi zed communication protocols , further broadening its applicability . The server is designed to support simultaneous monitoring of multiple operating locations and distinct third- party customers . This capability is facilitated by the server' s ability to manage separate records for each location and customer in its location database , ensuring that data and noti fications are appropriately segmented and tailored . This scalability makes the system particularly well-suited for use in large-scale logistics networks , where consistent and reliable monitoring is essential .

[0031] The condition database also supports the storage of derived data trends , calculated from historical condition records . These trends provide additional insights into the performance and health of equipment , enabling the detection of gradual degradation that might not be immediately apparent from individual condition records . For instance , a slow but steady increase in average humidity levels over several weeks could indicate a failing seal in a storage unit . By identi fying such

[0032] 10 trends , the system enables proactive maintenance , preventing minor issues from escalating into catastrophic failures .

[0033] Another notable feature of the invention is its ability to operate as a service for third-party customers . The server' s design allows it to integrate seamlessly with independently provided sensor units , provided they adhere to standardi zed communication protocols . This flexibility enables the system to of fer failure detection and noti fication services without requiring customers to adopt speci fic hardware . By decoupling the server' s functionality from the sensor hardware , the invention maximi zes its commercial applicability and expands its potential user base .

[0034] The server' s noti fication system is also highly customi zable . Noti fications can be prioriti zed based on the severity of detected failures or their proximity to critical thresholds . For example , a noti fication indicating an imminent compressor failure might be flagged as high-priority and delivered via multiple channels to ensure prompt action . The system can also log detected failures and the associated remedial actions in its databases , creating a historical record that can be used for future predictive analyses .

[0035] The invention addresses signi ficant deficiencies in prior art solutions . Unlike systems that rely on direct integration with equipment diagnostics , this invention operates independently, eliminating the need for complex and costly installations . It also goes beyond simple threshold-based alerts by incorporating advanced analytics and machine learning, enabling it to identi fy both immediate and evolving failure conditions . Additionally, its scalability and flexibility make it suitable for a wide

[0036] 11 range of applications, from single-site operations to global logistics networks.

[0037] The ability to dynamically adjust thresholds and analyze trends further enhances the invention's adaptability. For example, in a multi-site operation where ambient conditions vary significantly between locations, the system can tailor its failure detection parameters to each site's unique characteristics. This capability ensures that the system remains effective in diverse environments, providing reliable and actionable insights regardless of the specific operating conditions.

[0038] The modularity of the sensor units and the server' s compatibility with third-party hardware also contribute to the system's versatility. Customers can deploy the system alongside their existing infrastructure without significant modifications, reducing barriers to adoption. This feature is particularly valuable for smaller operators or those managing heterogeneous fleets of equipment, where standardization is often impractical.

[0039] In conclusion, the present invention offers a comprehensive and innovative solution to the challenges of cold chain monitoring and equipment failure prediction. By combining independent sensor units, advanced server analytics, and flexible notification capabilities, the invention addresses key limitations in prior art systems while introducing significant enhancements. Its scalability, adaptability, and costeffectiveness make it an ideal choice for a wide range of applications, from localized operations to complex, multicustomer logistics networks. By enabling proactive maintenance and minimizing operational disruptions, the invention sets a new standard for cold chain monitoring technology.

[0040] 12 BRIEF DESCRIPTION OF THE DRAWINGS :

[0041] To easily identi fy the discussion of any particular element or act , the most signi ficant digit or digits in a reference number refer to the figure number in which that element is first introduced . The drawings enclosed are :

[0042] Figure 1 is a system architecture diagram showing the overall structure of an embodiment of the system of the invention;

[0043] Figure 2 is a schematic diagram of a server in accordance with the invention;

[0044] Figure 3 is a schematic diagram showing the components of a wireless sensor unit in accordance with the invention;

[0045] Figure 4 is a database structure diagram showing the components of the location and temperature databases ; and

[0046] Figure 5 is a flowchart showing the steps of one embodiment of the condition monitoring method of the present invention .

[0047] DETAILED DESCRIPTION :

[0048] The detailed description of the embodiments of the invention is provided in connection with the figures to ensure a comprehensive understanding of the invention' s scope and functionality . Each embodiment is detailed with speci fic reference to the components , features , and possible extensions

[0049] 13 that fall within the scope of the invention, including temperature monitoring and the optional tracking of additional environmental parameters such as humidity .

[0050] Referring to Figure 1 , the overall system architecture of the invention is depicted . The cold chain sensor network comprises multiple wireless sensor units 1 that communicate with a remote server 3 via a network 2 . The network 2 can include local area networks , wide area networks , or combinations thereof , supporting communication protocols such as LoRa, Wi-Fi , Bluetooth, or cellular networks . Each sensor unit 1 is designed to independently capture environmental condition data, including temperature and optionally humidity or other parameters , which are transmitted to the server for storage as timestamped capture records . These sensor units 1 may operate within the same or di f ferent operating locations , enabling the system to provide comprehensive monitoring across diverse environments and operating locations .

[0051] The sensor units 1 operate independently of the cold-chain climate equipment at their respective operating locations , capturing environmental conditions in proximity to the equipment without direct integration . This independence enhances scalability and reduces deployment complexity, as the system can function seamlessly in heterogeneous environments .

[0052] The captured data is transmitted periodically to the remote server 3 , which includes memory components such as a location database 5 and a condition database 6 . These databases 5 , 6 organi ze and store the received environmental condition capture data for subsequent analysis .

[0053] 14 The server 3 is equipped with software 4 capable of administering the method of the invention, analyzing the condition records to predict potential equipment failures based on deviations from predefined thresholds , baseline values , or trends . Failure detection and noti fication are key aspects of the system, with the server 3 generating alerts for both immediate and time-ordered failure conditions . Noti fications are transmitted to relevant user devices 10 , enabling proactive responses and reducing downtime . This approach ensures consistent monitoring and early detection, even when sensor units 1 are deployed across geographically dispersed or varied operational sites .

[0054] Figure 2 illustrates the internal components of the server 3 . The server comprises a processor 20 , memory 21 , and a network interface 7 . The memory 21 hosts the method software 41 , which governs the operation of the server, including data reception, synchroni zation, integrity checks , and optionally, breach detection . The location database 5 and the condition database 6 are also stored in the memory 21 . The network interface 7 enables bidirectional communication between the server 3 and the wireless sensor units 1 , as well as between the server and client devices 10 . The modular design of the server allows for scalability, enabling the addition of processing power or memory as needed to support large deployments . To support configuration, the server further includes a user interface module that allows users to assign wireless sensor units to speci fic locations and configure noti fication settings . This interface can be accessible via a web portal , mobile application, or other devices , simpli fying the setup and ongoing management of the system . The general embodiment of the server shown will be understood to be variable to those skilled in the

[0055] 15 art - any type of a server with the necessary software and network connection will be within the scope of those elements of the present invention .

[0056] Referring to Figure 3 , the wireless sensor unit 1 is shown in greater detail . Each unit includes a power supply 30 , hardware components 31 , and memory / sof tware 32 for operation . A memory buf fer 33 stores capture records locally, ensuring that data is not lost during periods of network unavailability . The network interface 34 enables communication with the remote server 3 and supports multiple protocols , providing deployment flexibility in diverse environments . A temperature sensor 35 captures environmental temperature data, while modular configurations allow for the integration of additional sensors to measure parameters such as humidity, pressure , or light intensity . The wireless sensor unit 1 is designed to be mobile and robust , with a durable housing suitable for use in various cold chain scenarios , including pharmaceutical storage and agricultural applications . Additionally, the sensor unit ' s modularity allows for ef ficient maintenance and adaptability, ensuring extended usability and reduced costs . The local storage capability of the memory buf fer 33 is shown, providing reliable data management and ensuring seamless synchroni zation with the server during network availability . A memory buf fer 33 is however an optional component of the sensor unit 1 and necessary modi fications to the hardware and software of the invention to operate without memory buf fer storage at the sensor units 1 will also be understood to be within the scope of the present invention .

[0057] Importantly, the invention contemplates a wide variety of hardware configurations for the wireless sensor units 1 . The si ze , shape , and speci fic components of the sensor units can

[0058] 16 vary to accommodate di f ferent deployment scenarios . For example , sensor units designed for use in pharmaceutical storage environments might include higher-precision temperature sensors and enhanced humidity monitoring capabilities , while sensor units for agricultural applications may focus on additional metrics such as soil moisture or ambient light . Housing materials for the units can also vary, ranging from lightweight plastic casings for indoor use to reinforced, weather-resistant enclosures for outdoor deployments . Additionally, the power supply may be tailored to the use case , including replaceable or rechargeable batteries , solar panels , or hybrid configurations to extend operational li fe in resource-constrained environments . These variations ensure that the invention remains adaptable to a broad range of industries and applications .

[0059] In some embodiments , each wireless sensor unit 1 includes a unique serial identi fier that is stored in the location database 5 on the server 3 . This identi fier facilitates the association of captured data with speci fic sensor units and their deployment locations . In alternative embodiments , the system operates without unique identi fiers , relying on contextual or locationbased information to manage data records . The memory buf fer 33 ensures uninterrupted data capture by temporarily storing readings during network outages . Once connectivity is restored, the stored capture records are transmitted to the server 3 , where timestamps are compared against existing database records to identi fy and store any previously unrecorded data . This process ensures data integrity and completeness , particularly in scenarios with intermittent connectivity . The reliance on the memory buf fer as a central feature highlights the robustness and adaptability of the invention across diverse operational environments .

[0060] 17 The failure detection function and the associated software on the server are critical components of the invention, enabling early identi fication and proactive mitigation of potential equipment failures in cold chain systems . The server' s software is designed to analyze incoming environmental condition data transmitted from wireless sensor units , utili zing advanced algorithms , historical data comparisons , and dynamic thresholding to detect deviations that may indicate equipment performance issues or impending failures .

[0061] The failure detection function begins with the receipt of data and creation of condition records from the wireless sensor units . Each record includes environmental condition readings such as temperature or humidity, a timestamp, and the identi fier of the transmitting sensor unit . Upon receipt , the server' s software processes these records and stores them in the condition database . The software cross-references these records with the location database , which contains details about the corresponding operating locations , equipment types , thresholds , and historical baseline values . This cross-referencing ensures that each data point is contextuali zed within its speci fic environment , enabling accurate analysis and failure prediction .

[0062] The server' s failure detection function can employ multiple analytical approaches to identi fy potential issues . One method involves comparing individual condition values to predefined thresholds or baseline ranges stored in the location database . For instance , i f a refrigeration unit ' s acceptable temperature range is defined as 2-8 ° C, any condition value exceeding these limits is flagged as an out-of-range condition . These immediate breaches trigger noti fications to designated user contacts ,

[0063] 18 allowing for swi ft corrective actions . This threshold-based detection is particularly ef fective for identi fying acute issues , such as sudden equipment mal functions or environmental disruptions .

[0064] In addition to threshold comparisons , the software analyzes time-ordered ranges of condition values to detect gradual or evolving failure conditions . For example , the server may identi fy a slow upward trend in average temperature readings over several days . While each individual reading may remain within the acceptable range , the trend could indicate declining ef ficiency in a refrigeration unit , such as a failing compressor or insuf ficient coolant levels . By identi fying these evolving patterns , the system enables proactive maintenance , preventing minor issues from escalating into catastrophic failures .

[0065] The use of historical data stored in the condition database is a cornerstone of the failure detection function . The server' s software leverages historical records to establish baselines and identi fy deviations from typical operational patterns . For instance , a storage unit might exhibit minor daily temperature fluctuations due to operational cycles . By analyzing historical data, the server can di f ferentiate these normal variations from abnormal trends that signal potential issues . This capability is particularly valuable in environments with dynamic operational conditions , where static thresholds may be insuf ficient to account for all scenarios .

[0066] The failure detection function is also designed to accommodate dynamic adj ustment of thresholds and parameters . For instance , i f an operating location experiences a seasonal increase in ambient humidity, the server can recalibrate its thresholds to

[0067] 19 account for this change without compromising its sensitivity to deviations. This adaptability ensures that the system remains effective across diverse and evolving environmental conditions. Dynamic adjustments can be based on predefined rules, user input, or insights generated by machine learning models, further enhancing the system's flexibility and accuracy.

[0068] Notifications generated by the failure detection function are a critical aspect of the system. When a potential failure is identified, the server transmits notifications to the user contacts specified in the location database. These notifications are customizable, allowing users to define their preferred delivery methods, such as SMS, email, or application-based alerts. The notifications can also include detailed information about the detected issue, such as the specific condition value, the affected operating location, and recommendations for remedial actions. For example, a notification might advise users to inspect a refrigeration unit for coolant levels or mechanical issues based on observed temperature trends.

[0069] Derived data trends stored in the condition database can further enhance the failure detection function. These trends are calculated from historical condition records and provide additional insights into equipment performance and health. For instance, a slow but consistent increase in average temperature readings over several weeks could indicate a failing seal or insulation in a storage unit. By identifying these trends, the system enables proactive maintenance, preventing minor issues from escalating into more severe problems. The storage and analysis of derived trends also allow users to track the effectiveness of remedial actions, ensuring that identified issues are fully resolved.

[0070] 20 The failure detection function is designed to work seamlessly with sensor units that either have or lack data buf fers . Sensor units equipped with memory buf fers store captured condition data locally during network outages , ensuring uninterrupted data collection . Once connectivity is restored, these units transmit the stored data to the server for analysis . In contrast , sensor units without buf fers transmit data directly to the server in real-time . The software is compatible with both types of hardware , ensuring that the failure detection function remains ef fective regardless of the speci fic data capture methodology .

[0071] The server' s software is compatible with standardi zed communication protocols , allowing it to process data from a wide variety of sensor types and manufacturers . This interoperability enables third-party customers to use their existing hardware while benefiting from the advanced failure detection and noti fication capabilities of the system . By decoupling the server' s functionality from speci fic sensor hardware , the invention maximi zes its commercial appeal and expands its potential user base .

[0072] Referring to Figure 4 , the data structures for the location database 5 and the condition database 6 are illustrated, demonstrating one embodiment of the server data store . While depicted as separate entities for clarity, the invention is not limited to this speci fic structure . Alternative data configurations , such as a uni fied database combining location and condition information into a single table or file , are fully contemplated within the scope of the invention . For example , a uni fied data structure could store fields for operating location details , associated sensor data, captured environmental

[0073] 21 readings , and timestamps in one record, simpli fying data retrieval and management for certain system implementations . Similarly, non-relational database schemas , such as NoSQL structures , could be employed to accommodate large-scale deployments or optimi ze data performance for speci fic applications , such as real-time analytics in expansive coldchain operations . These flexible configurations demonstrate the invention' s adaptability to varying backend requirements and technological advancements .

[0074] Despite variations in the database structure , the fundamental functionalities of the system remain unchanged . The server consistently provides critical services , including data synchroni zation, failure detection, and user noti fication, ensuring that the system delivers the desired outcomes of reliable monitoring and robust data management . The client / server method between the wireless sensor units and the server is preserved regardless of the underlying database schema, maintaining the invention' s ability to monitor environmental conditions and predict equipment failures ef fectively . By supporting diverse data storage approaches , the invention ensures compatibility with evolving technological standards and operational needs across a range of industries .

[0075] The location database 5 as depicted in Figure 4 includes a field indicating range parameters for individual sensor units 9 . This field stores information about configured ranges , such as maximum allowable values or thresholds , used to predict or detect potential equipment failures . The inclusion of such range parameters allows the system to dynamically assess the captured environmental data against pre-configured benchmarks or historical trends . For instance , i f the environmental readings

[0076] 22 for a speci fic sensor unit approach a predefined threshold, the server' s failure detection function can identi fy this as a potential issue and generate a noti fication for the user . This predictive capability provides an early warning system for operators , enabling them to address potential problems before they result in equipment downtime or product spoilage .

[0077] The condition database 6 complements the location database by storing detailed condition records transmitted by the wireless sensor units . Each condition record includes fields for the timestamp of the data capture , the captured environmental condition readings , and the identi fier of the sensor unit that transmitted the data . These records are organi zed and queried by the server to perform the failure detection function . The server may analyze a series of condition records to identi fy trends or deviations from expected values , such as gradual increases in temperature or humidity levels that could indicate a developing equipment issue . By maintaining this historical data, the system supports both immediate noti fications for threshold breaches and long-term analysis to detect trends indicative of equipment degradation .

[0078] In alternative embodiments , the condition and location databases may be extended to store additional metadata, such as contextual information about the operating environment or sensor configuration details . For instance , the databases could include fields for ambient environmental conditions , equipment maintenance history, or calibration data for individual sensor units . These enhancements would enable more sophisticated analyses , allowing the server to tailor failure detection algorithms to the speci fic characteristics of each operating location . Such extensions further illustrate the flexibility and

[0079] 23 scalability of the invention, ensuring its applicability in diverse operational scenarios .

[0080] Referring to Figure 5 , the flowchart outlines a detailed sequence of steps illustrating one embodiment of the condition monitoring method of the present invention . This figure serves as a visual representation of the systematic approach used by the server and sensor units to predict equipment failures in a cold chain environment and noti fy users proactively .

[0081] The method begins with the receipt of condition data transmitted from wireless sensor units . Each sensor unit captures environmental condition readings , such as temperature or humidity, and transmits these readings to the server . Upon receipt , the server software creates a corresponding condition record in the condition database , as shown at step 5- 1 . Each record includes the timestamp of the captured data, the speci fic condition values , and identi fication details of the transmitting sensor unit . This process ensures comprehensive and structured data storage , enabling robust analysis and failure prediction .

[0082] At step 5-2 , the software determines whether a failure detection function should be executed . This decision can be triggered periodically based on a predefined schedule , a user-defined configuration, or an automated event such as detecting a trend in the data that requires immediate analysis . I f no such trigger exists , the system resumes its monitoring loop, waiting for the next event .

[0083] I f the trigger conditions are met , the server proceeds to step 5-3 , where it identi fies a historical window of condition records from the condition database for analysis . This window

[0084] 24 may be defined as all records since the last execution of the failure detection function or a fixed timeframe tailored to the speci fic operational context . For instance , some cold chain environments may benefit from a shorter analysis window, such as 30 minutes , while others may require a longer window to detect gradual deviations over days or weeks .

[0085] Once the appropriate historical window is established, the server software queries the condition database at step 5-4 to retrieve all relevant records . These records are then analyzed in step 5-5 , where the software compares individual condition values to predefined thresholds and baseline data stored in the location database . Additionally, the software examines time- ordered ranges of condition values to detect trends or patterns indicative of potential failures . For example , a slow increase in average temperature readings over several hours might signal a declining refrigeration system .

[0086] I f the analysis at step 5-5 identi fies any deviations or trends that suggest an impending failure , the server generates a user noti fication at step 5- 6 . The noti fication includes details about the detected issue , such as the speci fic environmental condition, the af fected operating location, and recommendations for corrective actions . Noti fications can be transmitted via multiple channels , including SMS , email , or application-based alerts , ensuring timely delivery to the appropriate recipients .

[0087] In cases where no failures are detected, the system completes the monitoring loop and resumes normal operation, ready to process new condition data as it is received . This continuous and adaptive process ensures that the system remains responsive to evolving conditions in the monitored environments .

[0088] 25 To enhance the flexibility and scalability of the method, modi fications and enhancements are considered within the scope of the invention . For instance , the historical window used for analysis can be dynamically adj usted based on seasonal variations or operational changes in the cold chain environment . Similarly, the failure detection algorithms can incorporate machine learning techniques to refine predictions and identi fy more complex patterns in the data . These enhancements allow the method to adapt to diverse and changing conditions , ensuring its ef fectiveness in a wide range of applications .

[0089] The server can also accommodate additional environmental parameters , such as air pressure or vibration, captured by advanced sensor units . These parameters are processed in the same manner as temperature or humidity data, with condition records created and analyzed for deviations or trends . This capability broadens the applicability of the invention, enabling it to address monitoring needs beyond traditional cold chain logistics .

[0090] By maintaining modularity and extensibility, the system provides a robust framework for monitoring critical environmental conditions and predicting equipment failures , ensuring reliability and ef ficiency across diverse operational contexts . The detailed steps outlined in Figure 5 reflect the invention' s emphasis on adaptability, scalability, and proactive maintenance .

[0091] In addition to temperature , the system can capture and monitor other environmental parameters , such as humidity . Humidity readings can be tracked using the same method described for

[0092] 26 temperature , with condition records transmitted to the server 3 for processing and storage . This capability expands the utility of the invention, enabling its application in scenarios where multiple environmental conditions must be monitored simultaneously . For instance , maintaining both temperature and humidity within speci fic ranges is critical in pharmaceutical storage and agricultural operations , ensuring product quality and safety . The adaptability of the memory buf fer to handle multiple types of data highlights its central role in the invention' s architecture .

[0093] The detailed embodiments described herein provide a robust framework for implementing the invention in a variety of settings . The combination of local data storage , modular design, advanced server functionality, and user-configurable interfaces addresses key challenges in environmental monitoring, ensuring reliable data capture , storage , and reporting in cold chain logistics and beyond . These features , combined with the flexibility to capture additional environmental parameters , make the invention a versatile and future-ready solution for monitoring critical conditions across diverse applications . By accommodating customi zable configurations and emerging technologies , the invention ensures its long-term utility and relevance in an evolving technological landscape .

[0094] 27

Claims

CLAIMS :1 . A cold chain sensor monitoring method for early equipment failure detection using a system comprising : a . a remote monitoring server comprising : i . memory including :1 . a location database storing records of operating locations , equipment type data with thresholds and baseline values , and user contact details for noti fications ; and2 . a condition database storing condition records , including timestamps , sensor readings , and associated sensor unit identi fiers ; ii . software to analyze condition records for potential equipment failures and generate user noti fications ; iii . a wireless network interface for two-way communication with user devices and at least one one wireless sensor unit configured to capture environmental condition data, each unit comprising :1 . a power supply and operating hardware and software ;282 . an environmental condition sensor to capture at least temperature or humidity; and3 . a wireless network interface for communication with a remote monitoring server ; wherein the method comprises : a . receiving condition readings from the sensor units and storing the related timestamps , sensor readings , and associated sensor unit identi fiers to condition records in the condition database ; b . periodically executing a failure detection function by querying condition records within a defined historical window to identi fy : a . condition values suggesting anticipated equipment failures based on deviations from baseline data or thresholds ; or b . time-ordered ranges of condition values suggesting anticipated equipment failures ; c . i f any equipment failure is predicted by the failure detection function, transmitting failure noti fications to the user contacts in the corresponding location record;wherein the sensors are independent of and not directly connected to the cold-chain climate equipment at the operating location; wherein the system provides noti fications of both individual and time-ordered failure conditions for early detection and repair .2 . The cold chain sensor method of Claim 1 , wherein the number of operating locations is one .3 . The cold chain sensor method of Claim 1 , wherein the number of operating locations is more than one .4 . The cold chain sensor method of Claim 1 , wherein the wireless sensor units capture temperature readings at their operating location .5 . The cold chain sensor method of Claim 1 , wherein the wireless sensor units capture humidity readings at their operating location .6 . The cold chain sensor method of Claim 1 , wherein the failure detection function further comprises comparing the captured condition values of each detection range record to the type data for the linked location record to identi fy any detection range records having captured conditionvalues outside the desired operating range at the operating location, in which case an equipment failure breach exists .7 . The cold chain sensor method of Claim 1 , wherein the failure detection function operates in a defined periodic frequency for the operating location, and the desired historical condition checking window used in the capture record query is the defined time window of said periodic frequency .8 . The cold chain sensor method of Claim 1 , wherein the failure detection function tracks the time-stamp of its periodic execution in respect of the operating location, and the desired historical condition checking window used in the capture record query is the time from the tracked time-stamp to the present .9 . The cold chain sensor method of Claim 1 , wherein the detection range records contain captured condition values within the desired operating range of the related coldchain climate equipment at the operating location, which when sorted in time order still indicate a range of values suggesting an anticipated equipment failure breach exists , resulting in the need for a user breach noti fication .10 . The cold chain sensor method of Claim 1 , wherein the failure detection function employs machine learningalgorithms to analyze historical condition records and predict potential equipment failures .11 . The cold chain sensor method of Claim 1 , wherein the noti fication includes recommendations for remedial actions based on the detected equipment failure breach .12 . The cold chain sensor method of Claim 1 , wherein the desired historical condition checking window is dynamically adj ustable based on the type data of the operating location .13 . The cold chain sensor method of Claim 1 , wherein the detection range records include derived data trends calculated from the captured condition values , enabling detection of gradual equipment failure .14 . The cold chain sensor method of Claim 1 , wherein the failure detection function identi fies deviations from historical trends stored in the condition database to predict potential failures .15 . The cold chain sensor method of Claim 1 , wherein the server dynamically adj usts its noti fication thresholds based on the equipment type data and environmental context provided in the location database .3216. A remote monitoring server for use in a cold chain system, comprising: a. a memory configured to store: i. a location database including:

1. records of operating locations and associated wireless sensor units;2. associated equipment type data with thresholds, baseline values, and operating ranges; and3. user contact details for notification; ii. a condition database comprising condition records with environmental condition values, timestamps, and identification details of associated wireless sensor units; b. a wireless network interface for two-way communication with wireless sensor units and user devices; c. software configured to, in a failure notification module : i. analyze the condition records in the condition database ;33ii . compare environmental condition values to thresholds and baseline data stored in the location database to detect potential equipment failures ; and iii . transmit failure noti fications to user devices based on detected failures .17 . The remote monitoring server of Claim 14 , wherein the software dynamically adj usts noti fication thresholds in the location database based on historical condition trends , changes in baseline data, or the operating context of the associated equipment .18 . The remote monitoring server of Claim 14 , wherein the condition database further stores derived data trends calculated from historical condition records to enable the detection of gradual equipment degradation .19 . The remote monitoring server of Claim 16 , wherein the software is configured to support simultaneous monitoring of multiple operating locations for distinct third-party customers .20 . A wireless sensor unit for use in a cold chain monitoring system, comprising : a . a power supply to enable independent operation;34b . at least one environmental condition sensor to capture at least one environmental parameter, including temperature or humidity; c . a wireless network interface to transmit environmental data to a remote monitoring server ; d . operating hardware and software configured to function independently of the equipment being monitored, to transmit captured condition values to said server for creation of capture records in a server-hosted database for failure prediction monitoring purposes ; wherein the sensor unit is not directly connected to the cold-chain climate equipment .

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